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Contingent Claims Analysis of Optimal Investment Decision Making in the Management of Timber Stands

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Contingent Claims Analysis of Optimal Investment Decision Making in the Management of Timber Stands
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2008

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Assets ( jstor )
Discount rates ( jstor )
Financial investments ( jstor )
Forests ( jstor )
Insurance risks ( jstor )
Investment risks ( jstor )
Market prices ( jstor )
Prices ( jstor )
Pulpwood ( jstor )
Timber ( jstor )

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CONTINGENT CLAIMS ANALYSIS OF OPTIMAL INVESTMENT DECISION
MAKING IN THE MANAGEMENT OF TIMBER STANDS














By

SHIV NATH MEHROTRA


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2006
































Copyright 2006

by

Shiv Nath Mehrotra
















ACKNOWLEDGMENTS

I am grateful to my supervisory committee chair, Dr. Douglas R. Carter, co-chair,

Dr. Janaki R. Alavalapati, and Drs. Donald L. Rockwood, Alan J. Long and Charles B.

Moss for their academic guidance and support. I particularly wish to thank Dr. Charles

Moss for always finding time to help with the finance theory as well as for aiding my

research in many ways.

I thank my family for their support and encouragement.




















TABLE OF CONTENTS


page

ACKNOWLEDGMENT S ................. ................ iii....... ....


LIST OF TABLES ........._.._... ..............vi..._.._........


LIST OF FIGURES .............. ....................vii


AB S TRAC T ......_ ................. ..........._..._ viii..


CHAPTER


1. INTRODUCTION ................. ...............1.......... ......


Economic Conditions in Timber Markets ................. ...............1............ ...
The Forest Industry in Florida ................ ...............2............ ...
Outline of the Investment Problem ................. ...............3............ ...
Research Objectives............... ...............

2. PROBLEM BACKGROUND .............. ...............9.....


Introduction to Slash Pine....................... .... ............
Slash Pine as a Commercial Plantation Crop .............. ...............9.....
Slash Pine Stand Density ................. ...............11................
Thinning of Slash Pine Stands............... ...............12.
Financial Background ............... ... ........... ............ .............1
The Nature of the Harvesting Decision Problem ................. .......................14
Arbitrage Free Pricing .............. ... .... ... ... ... .. .......... ...........1
Review of Literature on Uncertainty and Timber Stand Management. .................. ....20

3. THE CONTINGENT CLAIMS MODEL AND ESTIMATION
METHODOLOGY .............. ...............26....


The One-Period Model .............. ...............26....
The Deterministic Case .............. ...............26....
The Stochastic Case....................... ........................2
Form of the Solution for the Stochastic Value Problem. ........._..._.._ ........_.......3 1
The Contingent Claims Model ................. ...............31................
The Lattice Estimation Models............... ...............38.
T he B inomi al Latti ce M od el .............. ...............3 8....












The Trinomial Lattice Model for a Mean Reverting Process..............................42
The Multinomial Lattice Model for Two Underlying Correlated Stochastic
Assets ........._.___..... .__ ...............43....


4. APPLICATION OF THE CONTINGENT CLAIMS MODEL ................ ...............45


Who is the Pulpwood Farmer? ............ .... ...............45
The Return to Land in Timber Stand Investments................ ..............5
On the Convenience Yield and the Timber Stand Investment .............. ..................57

Dynamics of the Price Process ................. ......... ...............60.....
Modeling the Price Process .............. ...............63....
The Geometric Brownian Motion Process .............. ........... ...............6
Statistical Tests of the Geometric Brownian Motion Model ............... .... ...........67
The Mean Reverting Process............... .. .... ................7
Statistical Tests of the Mean Reverting Process Model .............. ................74
Instantaneous Correlation ............ .....___ ......__ ............7
The D ata..................... ... .... ..........7
Growth and Yield Equations .............. ...............76....
Plantation Establishment Expenses .............. ...............78....
Risk-Free Rate of Return ............ .....___ ...............79..
The Model Summarized .............. ...............79....


5. RE SULT S AND DIS CU SSION............... ..............8


A Single Product Stand and the Geometric Brownian Motion Price Process ............81
Sensitivity Analysis............... .... ... .. ...........8
Comparison with the Dynamic Programming Approach .................. ...............89
A Single Product Stand and the Mean Reverting Price Process ............... .............90
The Multiple Product Stand and Geometric Brownian Motion Price Processes........93
Thinning the Single Product Stand and the Geometric Brownian Motion Price
Process .............. ...............96....
D discussion .................. .. ....... .... ........... .............9
Recommendations for Further Research .............. ...............104....


APPENDIX CORRELATION OF FIRST DIFFERENCES OF AVERAGES OF
TWO RANDOM CHAINS................ ...............105


LI ST OF REFERENCE S ................. ...............107................


BIOGRAPHICAL SKETCH ................. ...............115......... ......

















LIST OF TABLES


Table pg

1-1. Comparison of applied Dynamic Programming and Contingent Claims
approaches ................. ...............6.................

2-1. Area of timberland classified as a slash pine forest type, by ownership class,
1980 and 2000 (Thousand Acres) .............. ...............10....

3-1. Parameter values for a three dimensional lattice ......____ ... ......_ ...............44

4-1. Florida statewide nominal pine stumpage average product price difference and
average relative prices (1980-2005) ...._.. ................ ............... 46 ....

4-2. The effect of timber product price differentiation on optimal Faustmann rotation...47

4-3. The effect of timber product relative prices on optimal Faustmann rotation ............47

4-4. Estimated GBM process parameter values for Florida statewide nominal
quarterly average pulpwood prices .............. ...............66....

4-5. Results of Jarque-Bera test applied to GBM model for Florida statewide nominal
quarterly average pulp wood stumpage prices .............. ...............70....

4-6. Inflation adjusted regression and MR model parameter estimates............._._... .........73

4-7. Results of Jarque-Bera test applied to MR model residuals for Florida statewide
nominal quarterly average pulpwood stumpage prices ................ ........._ ......75

4-8. Average per acre plantation establishment expenses for with a 800 seedlings/acre
planting density .............. ...............78....

5-1. Parameter values used in analysis of harvest decision for single product stand
with GBM price process............... ...............82

5-2. Parameter values used in analysis of harvest decision for single product stand
with MR price process............... ...............91

5-3. Parameter values used in analysis of harvest decision for multiproduct stand with
GBM price processes .............. ...............93....


















LIST OF FIGURES


Figure pg

1-1. Florida statewide nominal quarterly average pine stumpage prices (1976-2005 II
qtr) .............. ...............1.....

3-1. Typical evolution of even-aged stand and stumpage values for the Faustmann
analy si s............... ............... 2

4-1. Sample autocorrelation function plot for nominal Florida statewide pulpwood
stumpage instantaneous rate of price changes.. .........._..._ ................. ....._._.69

4-2. Sample autocorrelation function plot for nominal Florida statewide pulpwood
stumpage price MR model regression residuals ................. .....___ .........._.....75

5-1. Total per acre merchantable yield curve for slash pine stand............... ..................8

5-2. Crossover price line for single product stand with GBM price process...................83

5-3. Crossover price lines for different levels of intermediate expenses ........................85

5-4. Crossover price line for different levels of standard deviation .............. .................86

5-5. Crossover price lines for varying levels of positive constant convenience yield......87

5-6. Crossover price lines for different levels of current stumpage price.........................88

5-7. Crossover price line for single product stand with MR price process.......................92

5-8. Merchantable yield curves for pulpwood and CNS ................. ........................93

5-9. Crossover price lines for multiproduct stand ................. ...............95......_._. .

5-10. Single product stand merchantable yield curves with single thinning at different
ages ........._._. ._......_.. ...............97.....
















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

CONTINGENT CLAIMS ANALYSIS OF OPTIMAL INVESTMENT DECISION
MAKING IN THE MANAGEMENT OF TIMBER STANDS


By

Shiv Nath Mehrotra

August 2006

Chair: Douglas R. Carter
Cochair: Janaki R. Alavalapati
Maj or Department: Forest Resources and Conservation

The treatment of timber stand investment problems involving stochastic market

prices for timber and multiple options can be considerably improved by the application of

real options analysis. The analysis is applied to the dilemma of mature slash pine

pulpwood crop holders in Florida facing depressed markets for their product. Using a

contingent claims approach an arbitrage free market enforced value is put on the option

of waiting with or without commercial thinning, which when compared with the present

market value of stumpage allows an optimal decision to be taken.

Results for two competing models of timber price process support the decision to

wait for a representative unthinned 20-year-old cutover slash pine pulpwood stand with

site index 60 (age 25) and initial planting density 800 trees per acre. The present (III Qtr

2005) value of stumpage is $567/acre as compared to the calculated option value for the

Geometric Brownian motion price process of $966/acre and $1,290/acre for the Mean









Reverting price process. When the analysis differentiates the merchantable timber yield

between products pulpwood and chip-n-saw with correlated Geometric Brownian motion

price processes the option value rises to $1,325/acre for a stumpage market value of

$585/acre. On the other hand the commercial thinning option holds no value to the single

product stand investment when the poor response of the slash pine species to late rotation

thinning is accounted for.

The analysis shows that the measurement of option values embedded in the timber

stand asset is hampered by the lack of availability of market information. The absence of

a market for the significant catastrophic risk associated with the asset as well other non-

marketed risks also hampers the measurement of option values.

The analysis highlights the importance of access to market information for

optimal investment decision making for timber stand management. It concludes that stand

owners can realize the full value of the significant managerial flexibility in their stands

only when access to market information improves and markets for trading in risks

develop for the timber stand investment.

















CHAPTER 1
INTTRODUCTION

Economic Conditions in Timber Markets

Pine pulpwood prices in Florida have been declining since the peaks of the early

1990's (Figure 1-1). After reaching levels last seen in the early 1980's, in 2005 the prices

have shown signs of a weak recovery. The trend in pulpwood markets reflects the impact

of downturn in pulp and paper manufacturing resulting from several factors (Ince 2002)

like:


50.00-

45.00






O -*Saw Timber Price
S25.00 -a-Chip-n-saw Price
P- -Pulpwood Prices





5.00



Year

Source: Timber Mart-South


Figure 1-1. Florida statewide nominal quarterly average pine stumpage prices (1976-2005
II qtr)

1. A strong US dollar, rising imports and weakness in export markets since 1997.

2. Mill ownership consolidation and closures.









3. Increased paper recycling along with continued expansion in pulpwood supply
from managed pine plantations, particularly in the US South.

In a discussion of the findings and proj sections of the Resource Planning Act (RPA),

2000 Timber Assessment (Haynes 2003), Ince (2002) has noted that the pulp and paper

industry sector has witnessed a fall in capacity growth since 1998 with capacity actually

declining in 2001. The report proj ects that US wide pulpwood stumpage prices would

stabilize in the near term with a gradual recovery, but would not increase appreciably for

several decades into the future. With anticipated expansion in southern pine pulpwood

supply from maturing plantations, pine stumpage prices are proj ected to further subside

after 2015. Pine pulpwood stumpage prices are not proj ected to return to the peak levels

of the early 1990's in the foreseeable future (Adams 2002). Nevertheless, the US South is

proj ected to remain the dominant region in production of fiber products and pulpwood

demand and supply.

The Forest Industry in Florida

Florida has over 16 million acres of forests, representing 47% of the state' s land

area. Non-industrial private forest (NIPF) owners hold approximately 53% of the over 14

million acres of timberland in the state (Carter and Jokela 2002). The forest based

industry in Florida has a large presence with close to 700 manufacturing facilities. The

industry produces over 900,000 tons of paper and over 1,700,000 tons of paperboard

annually apart from hardwood and softwood lumber and structural panels (AF&PA

2003).

Pulpwood and sawlogs are the principal roundwood products in Florida accounting

for up to 80% of the output by volume. Pulpwood alone accounted for more than 50% of

the roundwood output in 1999. NIPF land contributed 45% of the total roundwood output









while an equal percent came from industry held timberlands. Slash and longleaf pine

provided 78% of the softwood roundwood output (Bentley et al. 2002).

Forest lands produce many benefits for their owners who express diverse reasons

for owning them. A survey of private forest land owners in the US South by Birch (1997)

found that nearly 3 8% of the private forestland owners hold forestland primarily because

it is simply a part of the farm or residence. Recreation and esthetic enj oyment was the

primary motive for 17% while 9% of the owners stated farm or domestic use as the most

important reason for owning forest land. Amongst commercial motives, land investment

was the primary motive for 12% of the owners. At the same time expected increase in

land value in the following 10 years was listed as the most important benefit from owning

timberland by 27% of landowners accounting for 21% of private forests listed.

Significantly, timber production was the primary motive for only 4% of the private

forestland owners, but these owners control 35% of the private forestland. Similarly, only

7% of the owners have listed income from the sale of timber as the most important

benefit in the following 10 years, but they control 40% of the private forest.

Outline of the Investment Problem

Timberland is defined as land that either bears or has the potential to bear

merchantable quality timber in economic quantities. The US has nearly 740 million acres

of forestland, of which 480 million acres is classified as timberland and the rest are either

preserves or lands too poor to produce adequate quality or quantity of merchantable

timber (Wilson 2000).

Small private woodlot ownership (<100 acres) accounts for more than 90% of

NIPF timberland holdings in the US and remains a significant part of the investment

pattern (Birch 1996). The prolonged depression in pulpwood prices poses a dilemma for









NIPF small woodlot timber cultivators in Florida who are holding a mature pulpwood

crop. These pulpwood farmers must decide about harvesting or extending the rotation.

The option to extend the rotation and wait out the depressed markets brings further

options like partial realization of revenues immediately through commercial thinnings.

These decisions must be made in the face of uncertainty over the future market prices)

for their timber productss. Slash pine pulpwood stand owners must also contend with the

fact that the species does not respond well to late rotation thinnings, limiting the options

for investing in late rotation products (Johnson 1961).

The timber stand investment is subj ect to several risks, marketed as well as non-

marketed (e.g., risk of damage to the physical assets in the absence of insurance).

Understanding and incorporating these risks into management decisions is crucial to

increasing the efficiency of the investment. The asset value/price risk is the most

common form of risk encountered by all investors. For most forms of investments

markets have developed several Einancial instruments for trading in risk. Insurance

products are the most common while others such as forwards, futures and options are

now widely used. Unfortunately, timberland investments lag behind in this respect.

Institutional timberland investors, with their larger resources, deal with specific risk by

diversification (geographic, product). Small woodlot owners must contend with the

greatest exposure to risk.

Investment risk in timber markets has been long recognized and extensively treated

in literature. As a result, on the one hand, there is a better appreciation of the nature and

importance of correctly modeling the stochastic variables, and on the other hand, there is

improved insight into the nature of the investment problem faced by the decision maker.










Despite the considerable progress, no single universally acceptable approach or model

has yet been developed for analyzing and solving these problems. Due to the Einancial

nature of the problem, developments in Einancial literature have mostly preceded progress

in forest economics research. In the last decades, the most important and influential

development in Einancial theory has been that of the option pricing theory. Several timber

investment problems are in the nature of contingent claims and best treated by the

application of option pricing theory or what is described as real options analysis (since

the investments are real as opposed to Einancial instruments).

It is known that for investment decisions characterized by uncertainty,

irreversibility, and the ability to postpone, investors set a higher hurdle rate. Stand

management decisions like commercial thinning and final harvest share these

characteristics. Options analysis provides a means for valuing the flexibility in these

investments. There are two approaches to options analysis, namely, the dynamic

programming (DP) approach and the contingent claims (CC) approach. Almost all

treatment of investment problems in forestry literature uses the DP approach to options

analysis. Despite its popularity in research, the applied DP approach has some drawbacks

which limit its utility for research or empirical applications. The CC valuation is free

from these limitations. Some important features of the application of the two approaches

are compared in Table 1.1.

The most critical problem is that application of the DP approach requires the

determination of an appropriate discount rate. In the absence of theoretical guidance on

the subj ect studies are forced to use arbitrary discount rates with little relation to the risk

of the asset. For example, Insley (2002) uses a discount rate of 5%, Insley and Rollins










(2005) use 3% and 5% real discount rates alternately, while Plantinga (1998) uses a 5%

"real risk-free" discount rate even though the analysis uses subj ective probabilities. No

justification is offered for the choice of the discount rate (Plantinga (1998) cites Morck et

al. (1989) for providing a rate "typical" to timber investment). Hull (2003) illustrates the

difference between the discount rate applicable to the underlying instrument and the

option on it. For a 16% discount rate applicable to the underlying, the illustration shows

that the discount rate on the option is 42.6%. Explaining the higher discount rate required

for the option, Hull (2003) mentions that a position on the option is riskier than the

position on the underlying. Another problem with the use of arbitrary discount rates is

that the results of different studies are not comparable.

Table 1-1. Comparison of applied Dynamic Programming and Contingent Claims
approaches


Dynamic Programming Approach


1. Requires the use of an externally
determined discount rate. This
discount rate is unobservable
(unless the option itself is traded).
The discount rates used in published
forestry literature bear no relation to
the risk of the asset.

2. Published forestry literature does
not specify whether the marketed,
the non-marketed or both components
of the asset's risk are being treated.


3. Risk preferences are treated
inconsistently in published forestry
literature .

4. Requires use of historical estimates
of mean return or drift which is
susceptible to large statistical errors.


Contingent Claims Approach


Uses a risk-free discount rate that
is reliably estimated from existing
market instruments.






Distinguishes between marketed and
non-marketed components of the
assets risk. Applies only to marketed
risk. Extensions have been proposed to
account for non-marketed risk

It is a risk neutral analysis.



Replaces the drift with the risk-free
rate of return. Estimates of
historical variance are relatively stable.









Similarly, none of the published research on options analysis in forestry specifies

whether the marketed, the non-marketed or both risks are being treated. Since the only

stochasticity allowed is in the timber price, it may be possible to infer that the marketed

risk is the obj ect of the analysis. But such inference would challenge the validity of some

of their conclusions. For example, Plantinga (1998) concludes that reservation price

policies, on an average, increase rotation lengths in comparison to the Faustmann

rotation, while management costs decrease rotation lengths. By including a notional cost

of hedging against non-marketed risks (insurance purchase) in the analysis as a

management cost any conclusion regarding the rotation extension effect of reservation

prices policies would be cast in doubt without better market data on the size of these

hedging costs.

Failure to highlight the treatment of risk preferences in the analysis is another

source of confusion. Some studies like Brazee and Mendelsohn (1988) specify that the

decision maker is risk neutral. Knowing this helps individuals to interpret the results

according to their risk preferences. But when risk preferences are not specified, as in

Insley (2002) for example, and there is confusion over the discount rate applied, the

results produced by the analysis lose interpretative value.

Real options analysis as it is applied through contingent claims valuation is itself a

nascent branch of the option pricing theory which has developed principally by extending

option pricing concepts to the valuation of real assets. There is increasing recognition of

the shortcomings of the techniques developed for pricing financial asset options when

applied to real assets and several modified approaches have been proposed. Nevertheless,

application of real options analysis to timber investment decisions offers an opportunity










to take advantage of a unified financial theory to treat the subject and thus obtain a richer

interpretation of the results.

Research Objectives

The general obj ective of this study is to apply contingent claims analysis to

examine typical flexible investment decisions in timber stand management, made under

uncertainty. The analysis is applied to the options facing the NIPF small woodlot owner

in Florida holding a mature even aged slash pine pulpwood crop. The specific obj ectives

are

1. To analyze and compare the optimal clear-cut harvesting decision for a single
product, i.e., pulpwood, producing stand with Geometric Brownian Motion
(GBM) and Mean Reverting (MR) price process alternately.

2. To analyze the optimal clear-cut harvesting decision for a multiple product, i.e.
pulpwood and chip-n-saw, producing stand with their prices following correlated
GBM processes.

3. To analyze the optimal clear-cut harvesting decision with an option for a
commercial thinning for a single product, i.e., pulpwood, producing stand with a
GBM price process.















CHAPTER 2
PROBLEM BACKGROUND

Introduction to Slash Pine

Slash pine (Pinus elliottii var. elliottii) is one of the hard yellow pines indigenous

to the southeastern United States. Other occasional names for the specie are southern

pine, yellow slash pine, swamp pine, pitch pine, and Cuban pine. Along with the most

frequently encountered variety P. elliottii var. elliottii the other recognized variety is P.

elliottii var. densa, which grows naturally only in the southern half of peninsula Florida

and in the Keys (Lohrey and Kossuth 1990).

The distribution of slash pine within its natural range (80 latitude and 100

longitude) was initially determined by its susceptibility to fire injury during the seedling

stage. Slash pine grew throughout the flatwoods of north Florida and south Georgia as

well as along streams and the edges of swamps and bays. Within these areas either ample

soil moisture or standing water protected young seedlings from frequent wildfires in

young forests (Lohrey and Kossuth 1990).

Slash pine is a frequent and abundant seed producer and is characterized by rapid

early growth. After the sapling stage it can withstand wildfires and rooting by wild hogs

which has helped it to spread to drier sites (Lohrey and Kossuth 1990).

Slash Pine as a Commercial Plantation Crop

Florida has the largest area of timberland (Barnett and Sheffield 2004) classified as

slash pine forest type (49%) while nonindustrial private landowners hold the largest

portion of slash pine timberland (Table 2-1)









Table 2-1. Area of timberland classified as a slash pine forest type, by ownership class,
1980 and 2000 (Thousand Acres)

Ownership Class 1980 2000

National Forest 522 493
Other Public 569 684
Forest Industry 4,649 3,719
Nonindustrial Private 7,039 5,479

Total 12,779 10,375

Source: Barnett and Sheffield, 2004

Slash pine makes rapid volume growth at early ages and is adaptable to short

rotations under intensive management. Almost three-fourths of the 50-year yield is

produced by age 30, regardless of stand basal area. Below age 30, maximum cubic

volume yields are usually produced in unthinned plantations, so landowners seeking

maximum yields on a short rotation will seldom find commercial thinning beneficial.

Where sawtimber is the obj ective, commercial thinnings provide early revenues while

improving the growth and quality of the sawtimber and maintaining the stands in a

vigorous and healthy condition (Lohrey and Kossuth 1990).

A study by Barnett and Sheffield (2004) found that a maj ority (59%) of the slash

pine inventory volume in plantations and natural stands was in the <10" dbh class while

about 25% of the stands were less than 8 years old. The study concluded that this

confirmed the notion that slash pine rotations are typically less than 30 years and that the

stands are intensively managed.

Plantation yields are influenced by previous land use and interspecies competition.

Early yields are usually highest on recently abandoned fields where the young trees

apparently benefit from the residual effects of tillage or fertilizer and the nearly complete

lack of vegetative competition. Plantations established after the harvest of natural stands









and without any site treatment other than burning generally have lower survival and,

consequently, lower basal area and volume than stands on old fields. Yields in plantations

established after timber harvest and intensive site preparation such as disking or bedding

are usually intermediate.

Comparing slash pine to loblolly pine (Pinus taeda L.), Shiver (2004) notes that

slash pine may be preferred over loblolly pine for reasons other than wood yields. For

instance, slash pine would be the favored species for landowners who want to sell pine

straw. Slash pine also prunes itself much better than loblolly, and for solid wood products

the lumber grade will probably be higher for slash pine. Slash pine is more resistant to

southern pine beetle (Dendroctonus frontalis Zimmermann) attack than loblolly and it is

rarely bothered with pine tip moth (Rhyacionia frustr anar~rtrt~t~r (Comstock)), which can

decimate young loblolly stands.

Slash Pine Stand Density

Dickens and Will (2004) discuss the effects of stand density choices on the

management of slash pine stands. The choice of initial planting density and its

management during the rotation depends on landowner obj ectives like maximizing

revenues from pine straw, obtaining intermediate cash flows from thinnings or growing

high value large diameter class timber products. High planting density in slash pine

stands decreases tree diameter growth as well as suppresses the tree height growth to a

lesser extent, but total volume production per unit of land is increased. However, the

volume increment observed for early rotation ages soon peaks and converges to that of

lower density stands as the growth rate of high density stands reach a maximum earlier.

Citing a study at the Plantation Management Research Cooperative, Georgia, Dickens









and Will (2004) remark that management intensity does not change the effects of stand

density.

Dickens and Will (2004) mention that higher density plantings achieve canopy

closure, site utilization, and pine straw production earlier than lower density plantings

under the same level of management. Higher planting densities also may be beneficial on

cut-over sites with low site preparation and management inputs. The higher planting

densities help crop trees occupy the site, whereas the lower planting densities may permit

high interspecific competition until much later during stand development, reducing early

stand volume production.

Thinning of Slash Pine Stands

Mann and Enghardt (1972) describe the results of subj ecting slash pine stands to

three levels of thinnings at ages 10, 13 & 16. Early thinnings removed the diseased trees

while later thinnings concentrated on release of better stems. Their study concluded that

early and heavy thinnings increased diameter growth but reduced volume growth. The

longer thinnings were deferred, the slower was the response in diameter growth. They

concluded that age 10 was too early for a thinning as most of the timber harvested was

not merchantable and volume growth was lost, even though the diameter increment

results were the best. The decision between thinning at ages 13 and 16 depended on the

end product, the ability to realize merchantable volumes in thinnings and the loss of

volume growth. They recommend that short rotation pulpwood crops were best left

unthinned as the unthinned stands had good volume growth. Quoting Mann and Enghardt

(1972) "volume growth is good, no costs are incurred for marking, there are fewer small

trees to harvest and stand disturbances that may attract bark beetle are avoided" (Mann

and Enghardt 1972, p.10).










Johnson (1961) has discussed the results of a study of thinning conducted on

heavily stocked industrial slash pine stands of merchantable size. The study found that

slash pine does not respond well to late release i.e., if it has been grown in moderately

dense stands for the first 20 to 25 years of its life. It does not stagnate, except perhaps on

the poorest sites, but it cannot be expected to respond to cultural treatments such as

thinnings as promptly or to the degree desired. Johnson (1961) observes that the typical

thinning operation that removes four to six cords of wood from well-stocked stands is

nothing more than an interim recovery of capital from the forestry enterprise. These

thinnings do not stimulate growth of the residual stand or total production. The study

found no real increase in total volume production or in average size of trees fr~om

commercial thrinning\ in slash pine stands being managed on short rotations for small

products.

Johnson (1961) concludes that silvicultural considerations for commercial thinning

in small product slash pine forest management are secondary to commercial

considerations because of its response to intermediate cuttings.

Financial Background

The timber farming investment exposes the investor to the risks that the asset

carries. These risks come in the form of marketed risks like the volatile market price for

the timber products or non-marketed risks that also effect the value of the investment

such as hazards that threaten the investment in the form of fire, pests, adverse weather

etc.

Usually, investors separate the spectrum of risks taken on by them from an

investment into core and non-core risks. The core risk could be the market price of the

investments output or product. This is the risk the investor expects to profit out of and









likes to retain. The non-core risk like the non-marketed risks listed above are undesirable

and the investor would ideally like to transfer such risks. A common market instrument

for risk transfer is the insurance product. By paying a price one can transfer the

undesirable risk to the market. If the non-marketed risks associated with the timber

investment were marketed, the market data available can be incorporated into investment

analysis. In the absence of markets for a part or all of an assets risk, the common asset

pricing theories are not applicable and alternate methods have to be applied. The analysis

in this study is restricted to the marketed risk in the form of timber price risk only.

The Nature of the Harvesting Decision Problem

Following a price responsive harvesting regime, the slash pine pulpwood farming

investor holding a mature crop and facing a stochastically evolving pulpwood market

price would like to know the best time for selling his crop. From his knowledge of past

movements of market price for pulpwood the investor knows that the present price is

lower than the average of prices in the recent past. He may sell the crop at the present

price but significantly he has the option to hold the crop. The crop is still growing, both

in size and possibly in value, and that provides incentive to hold the harvest. But the

market price is volatile. The future market price for pulpwood cannot be predicted with

certainty. How does the investor decide his immediate action; sell or hold?

While equilibrium asset values are determined by their productive capacities their

instantaneous market values are determined by the ever changing market forces. Asset

holders would like to eamn a fair compensation on their investment i.e., the principal plus

a return for the risk undertaken by holding the investment over time. But there is no

guarantee to earning a 'fair' return in the market place. Usually investors have a finite









time frame for holding an asset and must realize the best value for their asset in this

period.

The decision to hold the asset for a future sale date is a gamble, an act of

speculation. It carries the risk of loss as well as the lure of profit. But all investments in

risky assets are speculative activities. One investment may be more risky than another but

one market equilibrium theory in the form of the Capital Asset Pricing Model (CAPM)

assures us that their expected returns are proportional to their risk, specifically to the

systematic or non-diversifiable portion of their risk. The CAPM theory, development of

which is simultaneously attributed to Sharpe (1963, 1964) and Lintner (1965a, 1965b)

amongst others, has it that at any point in time each marketed asset has an associated

equilibrium rate of return which is a function of its covariance with the market portfolio

and proportional to the market price of risk. The expression 'rate of return' refers to the

capital appreciation plus cash payout, if any, over a period of time, expressed as a ratio to

the asset value at the commencement of the period.

If all risky investments are gambles, how does one choose amongst the enormous

variety of gambles that are available in the market place? Once again, financial theory

informs us that the choice amongst risky assets depends on the risk attitudes of

individuals. Individuals would apportion their wealth amongst a portfolio of assets

(which serves to eliminate the non-systematic risk of the assets). The portfolio is

constructed to match the risk-return tradeoff sought by the individual. Once chosen, how

does one decide how long to hold an asset? The risk associated with every asset as well

as its expected return changes over time. Over a period of time the risk-return









characteristic of a particular asset may lose its appeal to the individual's portfolio which

itself keeps changing with maturing of risk attitudes over time.

Returning to the pulpwood farmer' s decision problem, the question boils down to

this: How does the pulpwood farmer decide whether his investment is worth holding

anymore? It follows from the arguments above that the crop would be worth holding as

long it can be expected to earn a return commensurate with its risk. But, how is the

comparison between the expected rate of return and the required rate of return achieved?

The usual financial technique is to subj ectively estimate the expected cash flows from the

asset, discount them to the present using a risk-adjusted discount rate, and compare the

resulting value to the present market value of the asset. If the expected discounted value

is higher, then the expected rate of return over the future relevant period under

consideration is higher than the required rate of return. And how does this work? It works

because the required rate of return and the risk-adjusted discount rate are different names

for the same value. The expected equilibrium rate of return generated by the CAPM

represents the average return for all assets sharing the same risk characteristics or in other

words, the opportunity cost. When we use the risk-adjusted discount rate to calculate the

present value of the future cash flows, we are in effect accounting for the required rate of

return. The discounting apportions the future cash flows between the required rate of

return and residual value, if any.

Can discounted cash flow (DCF) analysis be used to solve the pulpwood farmer' s

harvesting problem? The pulpwood farmer' s valuation problem is compounded by the

ability to actively manage the investment (flexibility) or more specifically, the ability to

postpone the harvest decision should the need arise. Not only do decision makers have to









deal with an uncertain future market value for the pulpwood crop but they must also

factor in the response to the possible values. The termination date or harvest date of the

timber stand investment and thus its payoff is not fixed or predetermined. Traditional

DCF analysis can deal with the price uncertainty by the technique of subj ective

expectations but has no answer for flexibility of cash flow timings. This shortcoming has

been overcome by decision analysis tools like decision trees or simulation to account for

the state responsive future cash flows. So, are tools like decision trees or simulation

techniques the answer to the pulpwood farmer's dilemma? Almost, except that the

appropriate discount rate still needs to be determined.

Arbitrage Free Pricing

Despite widespread recognition of its shortcomings, the CAPM generated expected

rate of return is most commonly used as the risk-adjusted discount rate appropriate to an

investment. It turns out that while the mean-variance analysis led school of equilibrium

asset pricing does a credible job of explaining expected returns on assets with linear risk

they fail to deal with non-linear risk of the type associated with assets whose payoffs are

contingent. Hull (2003) provides an illustration to show that the risk (and hence discount

rates) of contingent claims is much higher than that of the underlying asset. The

pulpwood farmer holds an asset with a contingent claim because the payoff from his asset

over any period is contingent on a favorable price being offered by the market for his

crop.

There are two alternate though equivalent techniques for valuing a risky asset by

discounting its expected future cash flows. One, as already described involves an

adjustment to the discount rate to account for risk. The other method adjusts the expected

cash flows (or equivalently, the probability distribution of future cash flows) and uses the









risk-free rate to discount the resulting certainty equivalent of the future cash flows. The

CC valuation procedure follows this certainty equivalent approach. The argument is

based on the Law of One Price (LOP). The LOP argues that in a perfect market, in

equilibrium, only one price for each asset, irrespective of individual risk preferences, can

exist as all competing prices would be wiped out by arbitrageurs.

Baxter and Rennie (1996) illustrate the difference between expectation pricing and

arbitrage pricing using the example of a forward trade. Suppose one is asked by a buyer

to quote today a unit price for selling a commodity at a future date T A fair quote would

be one that yields no sure profit to either party or in other words provides no arbitrage

opportunities. Using expectation pricing, the seller may believe that the fair price to quote

would be the statistical average or expected price of the commodity, E [S, ], where S, is

the unit price of the commodity at time T and E is the expectation operator. But a

statistical average would turn out to be the true price only by coincidence and thus could

be the source of significant loss to the seller.

The market enforces an arbitrage free price for such trades using a different

mechanism. If the borrowing/lending rate is r then the market enforced price for the

forward trade is Sne'T This price follows the logic that it is the cost that either party

would incur by borrowing funds at the rate r to purchase the commodity today and store

it for the necessary duration (assuming no storage costs). This price would be different

from the expected price, yet offer no arbitrage opportunities.

The arbitrage free approach to the problem of valuing financial options was first

solved by Black and Scholes (1973) using a replicating portfolio technique. The

replicating portfolio technique involves finding an asset or combination of assets with









known values, with payoffs that exactly match the payoffs of the contingent claim. Then,

using the LOP it can be argued that the contingent claim must have the same value as the

replicating portfolio. Financial options are contingent claims whose payoffs depend on

some underlying basic Einancial asset. These instruments are very popular with hedgers

or risk managers.

The underlying argument to the equilibrium asset pricing methods is the no

arbitrage condition. The no arbitrage condition requires that the equilibrium prices of

assets should be consistent in a way that there is no possibility of riskless profit. A

complete market offers no arbitrage opportunities as there exists a unique probability

distribution under which the prices of all marketed assets are proportional to their

expected values. This unique distribution is called a risk neutral probability distribution

of the market. The expected rate of return on every risky asset is equal to the risk-free

rate of return when expectations are calculated with respect to the market risk neutral

di stributi on.

Copeland et al. (2004) define a complete market as one in which for every future

state there is a combination of traded assets that is equivalent to a pure state contingent

claim. A pure state contingent claim is a security with a payoff of one unit if a particular

state occurs, and nothing otherwise. In other words, when the number of unique linearly

independent securities equals the total number of alternative future states of nature, the

market is said to be complete.

Equilibrium asset pricing theories have been developed with a set of simplifying

assumptions regarding the market. In addition to completeness and pure competition, CC

analysis theory assumes that the market is perfect i.e., it is characterized by









1.An absence of transaction costs & taxes
2.Infinite divisibility of assets.
3.A common borrowing and lending rate.
4.No restrictions on short sales or the use of its proceeds.
5.Continuous trading.
6.Costless access to full information.


Review of Literature on Uncertainty and Timber Stand Management

The published literature on treatment of uncertainty in timber stand management is

reviewed here from an evolutionary perspective. A selected few papers are reviewed as

examples of a category of research.

The literature dealing with static analysis of financial maturity of timber stands is

vast and diverse. Including the seminal analysis of Faustmann (1849) several approaches

to the problem have been developed. The early work on static analysis has been

summarized by Gaffney (1960) and Bentley and Teeguarden (1965). These approaches

range from the zero interest rate models to present net worth models and internal rate of

return models. The Soil Rent/Land Expectation Value (LEV) model, also known as the

Faustmann-Ohlin-Pressler model, is now accepted as the correct static financial maturity

approach. However, the static models are built on a number of critical assumptions which

erode the practical value of the analysis. Failure to deal with the random nature of stand

values is a prominent shortcoming. Uncertain future values mean that the date of optimal

harvest cannot be determined in advance but must be price responsive i.e., it must depend

on the movement of prices and stand yield amongst other things. The harvest decision is

local to the time of decision and it is now recognized that a dynamic approach to address

the stochastic nature of timber values is appropriate.

Amongst the first to treat stochasticity in stand management, Norstom (1975) uses

DP to determine the optimal harvest with a stochastic timber market price. The stochastic









variable was modeled using transition matrices as in Gassmann (1988), who dealt with

harvesting in the presence of Gire risk. The use of transition matrices has persisted with

Teeter et al. (1993) in the determination of the economic strategies for stand density

management with stochastic prices.

However, much advance followed in modeling stochasticity with the introduction

of the use of diffusion processes in investment theory. Brock et al. (1982) illustrated the

optimal stopping problem in stochastic Einance using the example of a harvesting

problem over a single rotation of a tree with a value that grows according to a diffusion

process. Miller and Voltaire (1980, 1983) followed up, extending the analysis to the

multiple rotation problems. Clarke and Reed (1989) obtained an analytical solution using

the Myopic Look Ahead (MLA) approach, allowing for simultaneous stochasticity in

timber price and yield. These papers illustrate the use of stochastic dynamic

programming for stylized problems which are removed from the practical problems in

forestry e.g., they ignore the costs in forestry.

Modeling the empirical forestry problem, Yin and Newman (1995) modified Clarke

and Reed (1989) to incorporate annual administrative and land rental costs as exogenous

parameters. However, while acknowledging option costs, they chose to ignore them for

simplicity. Also, as noted by Gaffney (1960) the solution to the optimal harvest problem

is elusive because the land use has no predetermined cost and the solution calls for

simultaneous determination of site rent and financial maturity. Since land in forestry

investment is typically owned, not leased or rented, accounting for the unknown market

land rental has been one obj ective of Einancial maturity analysis since Faustmann (1849).









In the meanwhile, the use of search models to develop a reservation price approach

gained popularity with papers by Brazee and Mendelsohn (1988) and others. The

technique of the search models is not unlike the DP approach to contingent claims. The

approach differs from the CC approach in solution methodology and in the interpretation

of the results. Fina et al. (2001) presents an extension of the reservation price approach

using search models to consider debt repayment amongst other things.

Following the landmark Black and Scholes (1973) paper the development of

methodology for the valuation of contingent claims has progressed rapidly. A useful

simplification in the form of the discrete time binomial lattice to approximate the

stochastic process was presented by Cox et al. (1979). Other techniques for obtaining

numerical approximations have been developed including the trinomial approximation,

the finite difference methods, Monte Carlo simulations and numerical integration. Geske

and Shastri (1985) provide a review of the approximation techniques developed for

valuation of options.

An important simultaneous line of research has been the study of the nature of

stochasticity in timber prices. Washburn and Binkley (1990a) tested for weak form

efficiency in southern pine stumpage markets and reported that annual and quarterly

average prices display efficiency, but also point out that monthly averages display serial

correlation. Yin and Newman (1996) found evidence of stationarity in monthly and

quarterly southern pine time series price data. Since reported prices for timber are in the

form of period averages, researchers have to contend with unraveling the effect of

averaging on the statistical properties of the price series. Working (1960) demonstrated

the introduction of serial correlation in averaged price series, not present in the original









series. However, Haight and Holmes (1991) demonstrated that serially correlated

averaged price series tends to behave as a random walk. The lack of conclusive data on

the presence or absence of stationarity in timber price data is because of the imperfections

of the data available for analysis. Despite the lack of unanimity on the empirical

evidence there is some theoretical support for the mean reversion (negative

autoregression) arising from the knowledge that commodity prices could not exhibit

arbitrarily large deviations from long term marginal cost of production without feeling

the effects of the forces of demand and supply (Schwartz 1997).

The use of contingent claim analysis is a relatively recent development in stand

management literature. Morck et al. (1989) use real options analysis to solve for the

problem of operating a fixed term lease on a standing forest with the option to control the

cut rate. Zinkhan (1991, 1992) and Thomson (1992b) used option analysis to study the

optimal switching to alternate land use (agriculture). Thomson (1992a) used the binomial

approximation method to price the option value of a timber stand with multiple rotations

for a GBM price process. The paper demonstrates a comprehensive treatment of the

harvest problem, incorporating the option value of abandonment and switching to an

alternate land use.

Plantinga (1997) illustrated the valuation of a contingent claim on a timber stand

for the mean-reverting and driftless random walk price processes, using a DP approach

attributed to Fisher and Hanemann (1986). Yoshimoto and Shoji (1998) use the binomial

tree approach to model a GBM process for timber prices in Japan and solve for the

optimal rotation ages. Insley (2002) advocated the mean-reverting process for price

stochasticity. The paper incorporates amenity values and uses harvesting costs as an









exercise price to model the harvesting problem over a single rotation as an American call

option. In order to obtain a numerical solution, the paper uses a discretization of the

linear complementarity formulation with an implicit finite difference method. All these

studies use a stochastic DP approach with an arbitrary discount rate.

Hughes (2000) used the Black-Scholes call option valuation equation to value the

forest assets sold by the New Zealand Forestry Corporation in 1996. The option value

estimated by him was closer to the actual sale value than the alternate discounted cash

flow analysis. It is a unique case of a study applying real options analysis to value a real

forestry transaction.

Insley and Rollins (2005) solve for the land value of a public forest with mean

reverting stochastic timber prices and managerial flexibility. They use a DP approach to

show that by including managerial flexibility, the option value of land exceeds the

Faustmann value (at mean prices) by a factor of 6.5 for a 3% discount rate. The land

value is solved endogenously for an infinite rotation framework.

In a break from analysis devoted to the problems of a single product timber stand

Forboseh et al. (1996) study the optimal clear cut harvest problem for a multiproduct

pulpwoodd and sawtimber) stand with joint normally distributed correlated timber prices.

The study extends the reservation price approach of Brazee and Mendelsohn (1988) to

multiple products and looks at the effect of various levels of prices and correlation on the

expected land value and the probability of harvest at different rotation ages. A discrete

time DP algorithm is used to obtain the solutions.

In a similar study, Gong and Yin (2004) study the effect of incorporating multiple

autocorrelated timber products into the optimal harvest problem. The paper models the









timber prices pulpwoodd and sawtimber) as discrete first order autoregressive processes.

Dynamic programming is used to solve for reservation prices.

Teeter and Caulfied (1991) use dynamic programming to demonstrate the

determination of optimal density management with stochastic prices using a first order

autoregressive price process modeled using a transition probability matrix. The study

uses a Eixed rotation age and allows multiple thinnings. Brazee and Bulte (2000) analyze

an optimal even-aged stand management strategy with the option to thin (fixed intensity)

with stochastic timber prices. Using a random draw mechanism for the price process and

a backward recursive DP algorithm for locating the reservation prices, the study finds the

existence of an optimal reservation price policy for the thinning option. Lu and Gong

(2003) use an optimal stocking level function to determine the optimal thinning as well as

a reservation price function to determine the optimal harvest strategy for a multiproduct

stand with stochastic product prices without autocorrelations.















CHAPTER 3
THE CONTINTGENT CLAIMS MODEL AND ESTIMATION METHODOLOGY

The One-Period Model

In order to develop the application of options analysis to investment problems it is

helpful to first examine the nature of one-period optimization models. One-period models

for investment decision making operate by comparing the value of the investment in the

beginning of period with its value at the end of the period. The model is first explained in

the context of the deterministic Faustmann problem. This is followed by an extension of

the logic to the stochastic problem.

The Deterministic Case

The problem of finding the optimal financial stand rotation age is an optimal

stopping problem. In the deterministic Faustmann framework, the optimal rotation age is

achieved by holding the stand as long as the (optimal) investment in the stand is

compensated by the market at the required rate of return. The value of the immature stand

is the value of all net investments in the stand up to the present including the land rental

costs and the cost of capital. This means that the value of all investments in the stand

(adjusted for positive intermediate cash flows like revenue from thinnings) up to the

present compounded at the required rate of return represents the stand value. This value

represents fair compensation to the stand owner for his investment and fair cost to the

purchaser who would incur an identical amount in a deterministic world. Therefore, this

value represents the fair market value of the pre-mature stand. The market value of the

merchantable timber in the stand, if any, is less than the stand market value in this period.











The stand owner continues to earn the required return on his (optimal) investments

only till the rotation age is reached when the value of the merchantable timber in the

stand exactly equals the compounded value of investments. Beyond this rotation age the

market will only pay for the value of the merchantable timber in the stand. If the stand is

held longer than this rotation age, even if no fresh investments other than land rent are

made, the market compensation falls short of the compounded value of investments as the

value of merchantable timber grows at a lower rate. The optimal rotation age represents

the unique point of financial maturity of the stand. Before this age the stand is financially

immature and after this age the stand is financially over mature. A typical evolution of

the two values is depicted in Figure 3-1.


4500-

4000

3500

3000

S- -Value of Merchantable limber
t 2500

-* Present Value of net
S2000
investments

gj 1500

1000

500





Rotation age (Years)



Figure 3-1. Typical evolution of even-aged stand and stumpage values for the
Faustmann analysis










Equivalently, a more familiar way of framing this optimization problem is to let the

stand owner compare the value of harvesting the stand in the present period to the net (of

cost of waiting) discounted values of harvests at all possible future rotation ages. The cost

of waiting includes land rent and all other intermediate cash flows. More specifically, the

comparison is between the value of a harvest decision today and the net discounted value

of the stand in the next period assuming that similar optimal decisions are taken in the

future. In this case the stand value represents the discounted value of a future optimal

harvest which exactly equals the earlier defined stand market value consisting of net

investment value. Thus, the problem is cast as a one-period problem.

The one-period deterministic Faustmann optimization problem in discrete time can

be summarized mathematically by Equation 3-1.


F(t)= m;lnr, xlax ~ O+tr8) (3-1)


Here,

F()= Stand Value function
t = Rotation age
0Z = Stand termination value or the market value of the merchantable timber in the stand
ri = Rate of cash flow (land rental expenses, thinnings etc)
B = Constant discount rate
A = A discrete interval of time

For the period that the decision to hold the stand dominates, the second expression

in the bracket is relevant and we have for the holding period Equation 3-2.

F(t + At) 32



It may be noted that the only decision required of the decision maker is whether to

hold the stand or to harvest it. In the standard deterministic case, any intervention









requiring new investments like thinnings is assumed optimally predetermined and the

resulting cash flows are only a function of rotation age. This holding expression can be

simplified to yield Equation 3-3 for the continuous time


OF~t) rt+-Ftl) (3-3)


Here, the limit of A 4 0 has been taken. Equation 3-3 clearly expresses the holding

condition in perfect competition as one in which the yield (Right Hand Side (RHS)) in

the form of the dividend and the capital appreciation/depreciation or change in market

value over the next infinitesimal period equals the required rate of return on the current

market value of the asset (Left Hand Side (LHS)).

The optimal stopping conditions are

F(T) = OZ(T) (3 -4)

F,(T)= O,(T) (3-5)

In Equation 3-5 the subscript t denotes the derivative of the respective function

with respect to the time variable. The first condition is simply that at the optimal rotation

age Tthe market value should equal the termination value and the second condition is the

tangency or the smooth pasting condition (Dixit and Pindyck 1994) requiring that the

slopes of the two functions should be equal.

The Stochastic Case

In the stochastic value framework, the problem of optimal rotation is equivalent to

holding the asset as long as it is expected to eamn the required return. With stochastic

parameter values, not only are future asset values dependent on the realizations of the

parameters but the ability to actively manage the asset by responding to revealed

parameter values induces an option value. Dixit and Pindyck (1994) derive the holding









condition for the stochastic framework using the Bellman equation, which expresses the

value as


F(x,t) =max z~(x,u~,t)A(+(1+A) l-EF(x~t+ A)l |x~u) (3-6)

Here,

F()= Stand value function
x = The (vector of) stochastic variable(s). For this analysis it represents the timber
prices)
t = Rotation age
u = The control or decision variable (option to invest)
xi = The rate of cash flow
B = The discount rate
E = Expectation operator
A = A discrete interval of time

This relation means that the present value F(x, t) from holding the asset is formed

as a result of the optimal decision taken at the present, which determines the cash flow

ai in the next period A and the expected discounted value resulting from taking optimal

decisions thereon. Distinct from the deterministic case, in this case, the value (and

possibly cash flows) depends on the stochastic timber price. Also, the decision can be

expanded to include the decisions to make new investments in the stand (like thinning)

which effects the immediate cash flows as well as expectations of future market values.

Similar to the deterministic case, the holding condition can be re-expressed as


OF(x, t) = max zcx,u~~t)+ E FxL ) (3 -7)


To quote Dixit and Pindyck (1994):

The equality becomes a no arbitrage or equilibrium condition, expressing the
investor' s willingness to hold the asset. The maximization with respect tou means
the current operation of the asset is being managed optimally, bearing in mind not
only the immediate payout but also the consequences for future values. (Dixit and
Pindyck 1994, p.105)









Form of the Solution for the Stochastic Value Problem

In general the solution to the problem has the form of ranges of values of the

stochastic variable(s) x Continuation is optimal for a range(s) of values and termination

for otherss. But as elaborated by Dixit and Pindyck (1994), economic problems in

general have a structured solution where there is a single cutoff x* with termination

optimal on one side and continuation on the other. The threshold itself is a continuous

function of time, referred to as the crossover line. The continuation optimal side is

referred to as the continuation region and the termination optimal side as the termination

region. As pointed out by Plantinga (1998) the values of the crossover stumpage price

line for timber harvesting problems are equivalent to the concept of reservation prices

popular in forestry literature.

Consequently, the optimal stopping conditions for the stochastic case for all t are

(Dixit and Pindyck 1994)

F(x* (t), t) = O(x*(t), t) (3-8)

Fx (x* (t), t) = 2x (x*(t), t) (3-9)

In Equation 3-9 the subscript x denotes the derivative of the respective function

with respect to the variable x .

The Contingent Claims Model

In this section the general theory of CC valuation is developed in the context of the

harvest problem. The CC valuation approach is also built on a one-period optimization

approach and the discussion of the last section should help to put the following discussion

into perspective.









The simplest harvest problem facing the decision maker is as follows: Should the

stand be harvested immediately, accepting the present market value of the timber or

should the harvest decision be postponed in expectation of a better outcome? That is, the

possibility for all optimal interventions other than harvest is ignored. In a dynamic

programming formulation of the problem, using the Bellman equation, the problem can

be expressed mathematically as follows (Dixit and Pindyck 1994)

I 1
F~(x,ti)=max O(x,t), r(x,ti)+-18E[F(x,ti+1) |x] (3-10)

Here F(x, t) is the expected net present value of all current and future cash flows

associated with the investment at time t, when the decision maker makes all decisions

optimally from this point onwards. The stochastic state variable, timber price in the

present problem, is represented by x The immediate cash flow from a decision to hold

the investment is denoted by zi(x, t) The result of optimal decisions taken in the next

period and thereafter will yield value F(x, t + 1) which is a random variable today. The

expected value of F(x, t + 1) is discounted to the present at the discount rate B Finally,

OZ(x, t) represents the present value of termination or the value realized when the

investment is fully disposed off today.

While we know the present termination value, we are interested in learning the

value of waiting or the continuation value. If the decision to wait is optimally taken then

the continuation value is given by

_I 1
F(x, t) =z(x,t)+-+E [F(x,t +1)I |x] (3-11)

If the increments of time are represented by A and A 4 0, the continuation value

expressed in continuous time after algebraic manipulation will be










OF(x,ta)= z(x, t)+ -E[dF] (3- 12)


If it is assumed that the state variable x (timber price) follows a general diffusion

process of the form

dx~ = pU(x, t)dt + a(x, t)dz (3-13)

then, using Ito's Lemma, after algebraic manipulation and simplification we obtain the

partial differential equation (PDE)


-a2FXY+ pUFx + Ft BF + z= 0 (3-14)


Here, pu = pu(x, t), a = o(x, t) and ai = zi(x, t) In typical economic problems the

continuation equation will hold for the value of the asset for all x > x*, where x* is a

critical value of the state variable x with the property that continuation is optimal when

the state variable value is on one side of it and stopping or termination is optimal when

the state variable value is on its other side. This yields the boundary conditions for all t,

given by Equations 3-8 and 3-9, which the value of the asset must meet at the critical

value of the state variable

The DP formulation of the problem assumes that the appropriate discount rate B is

known or can be determined by some means. An equivalent formulation of the problem

can be found using CC valuation. In this form the PDE for the continuation region value

is given by


-G2FXY + (r 3)FX + Ft -rF + = 0 (3-15)


Here r represents the risk-free rate of return and 3 represents the rate of return

shortfall which could be a dividend and/or convenience yield. Dixit and Pindyck (1994)









illustrate the derivation of the contingent claim PDE by using the replicating portfolio

method. In an alternate general derivation the procedure is to first show that under certain

assumption all traded derivative assets must satisfy the no-arbitrage equilibrium

relation a-r = A re, Here a is the expected return on the derivative security, a,


represents the component of its volatility attributable to an underlying stochastic variable

i and ii, represents the market price of risk for the underlying stochastic variable. Where

there is only one underlying stochastic variable the relation simplifies to a r = Aso.

Constantinides (1978) derived the condition for changing the asset valuation

problem in the presence of market risk to one where the market price of risk was zero.

The derivation, presented below, proceeds from Merton' s (1973) proof of equilibrium

security returns satisfying the CAPM relationship


a -r =i 2pm (3-16)

where ii= (s-)is the market price of risk, the subscript p refers to the proj ect (asset,


option etc) and subscript m refers to the market portfolio which forms the single

underlying stochastic variable. Merton (1973) assumed that

1. The markets are perfect with no transaction costs, no taxes, infinitely divisible
securities and continuous trading of securities. Investors can borrow and lend at the
same interest rate and short sale of securities with full use of proceeds is allowed.

2. The prices of securities are lognormally distributed. For each security, the expected
rate of return per unit time as and variance of return over unit time 0z2 OXiSt and are
finite with 0,2 > 0 The opportunity set is non-stochastic in the sense that az >"22
and the covariance of returns per unit time o, and the riskless borrowing-lending
rate r are all non-stochastic functions of time.









3. Each investor maximizes his strictly concave and time-additive utility function of
consumption over his lifespan. Investors have homogenous expectations regarding
the opportunity set.

Let F(x, t) denote the market value of a proj ect. The market value is completely

specified by the state variable x and time t, and represents the time and risk-adjusted

value of the stream of cash flows generated from the proj ect. Let the change in the state

variable x be given by

dre = udt + odz (3-17)

The drift u and variance 02 may have the general form u = u(x, t) and

02 __ 2(x, t) .Let aidt denote the cash flow generated by the proj ect in time interval

(t, t + dt) with ai = zi(x, t) Then, the return on the proj ect in the time interval (t, t + dt) is

the sum of the capital appreciation dF(x, t) and the cash retuma~idt Assuming that the

function F(x, t) is twice differentiable w.r.t. x and atleast once differentiable w.r.t. t, Ito's

Lemma can be used to expand dF(x, t) as



dFi(xt)=( F+uF +-F,; dt + Fxdz (3-18)


The rate of return on the proj ect is

dF(x, t) +zidt__ 1 2 F
z+,+~ +-F dt+ dz (3-19)
F (x, t) F 2 : Fto

with expected value per unit time a,, and covariance with the market per unit time a,

given by

1 a2
a = F +F uF +-F ,; (3-20)
F 2










J, = pa, smnce p =
FF


where p = p(x, t) is the instantaneous correlation coefficient between &z and the return

on the market portfolio.

By substitution in the Equation 3-16 we obtain the PDE


- Fxx + (u-_ Alp)Fx + Ft -rF + 7r = 0 (3 -21)


First, it may be noted that p is the correlation coefficient between &z and the return

on the market portfolio. Since &z is the only source of stochasticity in the proj ect and the

underlying, p is also equal to the correlation coefficient between return on the

underlying and the return on the market portfolio.

Second, when compared with the DP formulation using the discount rate B it can

be seen that the CC analysis modifies the total expected rate of return pu by a factor of

Alpo which allows the use of the risk-free rate of return r In this manner the CC analysis

converts the problem of valuing a risky asset to one of valuing its certainty equivalent. It

does away with the need to determine the discount rate B but does require an additional

assumption regarding the completeness of the market or in other words only the marketed

risk of the asset can be valued.

Further, as shown in Hull and White (1988), if the state variable is a traded security

and pays a continuous proportional dividend at rate 3, then in equilibrium, the total

return provided by the security in excess of the risk-free rate must still be Alpo, so that;

pu + 3- r = Alpo (3 -22)











pu Alpe = r 3 (3-23)

Substituting in Equation 3-21 we obtain the PDE derived by Dixit and Pindyck

(1994) using the replicating portfolio i.e.,


cr2F, + (r 3)Fx + F rF + 7r = 0 (3 -24)


Hull (2003) differentiates between the investment and the consumption asset. An

investment asset is one that is bought or sold purely for the purpose of investment by a

significant number of investors. Conversely, a consumption asset is held primarily for

consumption. Commodities like timber are consumption assets and can earn a below

equilibrium rate of return. Lund and Oksendal (1991) discuss that generally investors will

not like to hold an asset that earns a below-equilibrium rate of return. But empirically

commodities that earn a below equilibrium rate of return are stored in some quantities. To

quote Lund and Oksendal (1991):

In order to explain storage of commodities whose prices are below-equilibrium, it
is assumed that the stores have an advantage from the storage itself. This is known
as gross convenience yield of the commodity. The net convenience yield (or simply
the convenience yield) is defined as the difference between the marginal gross
convenience yield and the marginal cost of storage. (Lund and Oksendal 1991, p.8)

If we assume a continuous proportional convenience yield then the assumption is

completely analogous to an assumption of a continuous proportional dividend yield.

Therefore, the 3 can represent the continuous proportional convenience yield from

holding the timber and the PDE will hold.









The Lattice Estimation Models

The Binomial Lattice Model

In order to determine the holding value of the asset i.e., the value in the

continuation region, it is necessary to solve the PDE. As it is not always possible to

obtain an analytical solution, several numerical procedures have been devised. Amongst

the popular methods for obtaining a numerical solution are the lattice or tree

approximations (that work by approximating the stochastic process) and the finite

difference methods, explicit and implicit (that work by discretizing the partial differential

equation). Monte Carlo simulations and numerical integration are other popular

techniques.

This study uses the lattice approximation approach for its simplicity and intuitive

appeal. Depending on the nature of the problem the binomial or higher dimension lattice

models were used. The binomial approximation approach is suitable for valuation of

options on a single underlying stochastic state variable and was first presented by Cox et

al. (1979). For an underlying asset that follows a GBM process of the form


= pudt + odz (3-25)


where the drift pu and the variance 02 are assumed constant, the binomial approach

works by translating the continuous time GBM process to a discrete time binomial

process. The price of a non-dividend paying underlying asset denoted by P is modeled to

follow a multiplicative binomial generating process. The current asset price is allowed to

either move up over the next period of length A by a multiplicative factor u to uP with

subjective probability p or fall by the multiplicative factor dto dP with probability









(1- p) To prevent arbitrage the relation u > 1+ r > d must hold where r represents the

risk-free interest rate. The asset price follows the same process in every period thereafter.

Following Ross (2002) it can be shown that, the binomial model approximates the

lognormal GBM process as A becomes smaller. Let Y equal 1 if the price goes up at time

iA and 0 otherwise. Then, in the first n increments the number of times the price goes up



is [Y and the asset price would be P = (d" u I


Letting n = gives




',P = d^

Taking logarithms we obtain


In( =,, In d + Y I n (3-26)


The Y are independent, identically distributed (iid) Bernoulli random variables

with mean p and variance p(1 p) Then, by the central limit theorem, the


summation I Y, which has a Binomial distribution, approximates a normal distribution

t t t
with mean p and variance p(1 p) as Abecomes smaller (and grows larger).


Therefore, the distribution of In converges to the normal distribution as ~hgrows.









Following the moment matching procedure Luenberger (1998) shows that the

derived expressions for the parameters ,d and p are



p =2 +1 Io 2 :" (3-27)




u =e" (3-28)

d =e "J (3-29)

The DP procedure for analysis of an option on an underlying asset that follows

GBM process would proceed by using a binomial lattice parameterized by these

expressions. The DP procedure would obtain the option value by recursively discounting

the next period values using the subj ective probability value p and an externally

determined discount rate.

In contrast the contingent analysis procedure is illustrated using the replicating

portfolio argument as follows. In addition to the usual assumptions of frictionless and

competitive markets without arbitrage opportunities, as noted earlier, it is assumed that

the price of a non-dividend paying asset denoted by P follows a multiplicative binomial

generating process. The asset price is allowed to either move up in the next period by a

multiplicative factor u or fall by the multiplicative factor d .If there exists an option on

the asset with an exercise price of X, then the present value of the option denoted by c

would depend on the contingent payoffs in the next period denoted by

cl = M4lX [0,ugq -X] and cd = M4X [0,d~o X] where 4 denotes the current price of

the asset.









In order to price the option a portfolio consisting of one unit of the asset and

na units of the option written against the asset is constructed such that the end of the

period payoff on the portfolio are equal i.e.,

upo -nac,, = dP, -nacd (3-30)

Solving for na we get


nz= 4(-)(3-31)
c,, cd

If the end of the period payoff is equal the portfolio will be risk free and if we

multiply the present value of the portfolio by l +r we should obtain the end of period

payoff

(1 + r)(P, mc) = uP, nac, (3-32)



P, [(1 +r) uj +nc
c = (3-33)
nz(1+r)

Substituting Equation 3-31 for na in Equation 3-33 yields


C='((1+ r)-d 1 edu-(1+r): (r 3-4
u-d u-d

Letting

(1+ r)- d
q = (3-35)
u-d

where q is known as the risk-neutralprobability/, we can express the present value of the

option as

c = [qc, + (1 -q)cd j+t(1 +r) (3 -3 6)









From Equations 3-28 and 3-29 we have u = e"i and d = e-" To find the value

of the risk-neutral probability q these values of u and d can be substituted in Equation

3-35 to obtain


(1+ r)- en 1 1 2
q~ = +- 0 (3-37)


Compared with the expression for the subjective probability p in Equation 3-27, it

can be seen that under the risk-neutral valuation the drift of the GBM process pu is

replaced by the risk-free rater .

In general, if the asset pays out a continuous proportional dividend 6 then, under

CC analysis the drift is modified tor 3 (Equation 3-24). The corresponding risk neutral

probability is


q=1 2 (3-3 8)
2 20

For the treatment of previsible non-stochastic intermediate cash flows (costs) with a

fixed value (si) the Equation 3-36 is modified to

c = [qcu + (1-q4)cd j-t(1+ r)+ zi (3-39)

It is implicit that i represents the discounted net present value of all such cash

flows in the period.

The CC procedure for a single period outlined above is easily extended to multiple

periods and the option value is derived by recursively solving through the lattice.

The Trinomial Lattice Model for a Mean Reverting Process

For an asset that follows a MR process of the form











dx= 9 x- x dt + odz (3 -40)

the contingent claims PDE is given by


-aF~U + [r- 3(x)]F~ + ,- rF + z = 0 (3-41)


where 3(x) = pu (x- x) i s a function of the underlying asset x .


Hull (2003) describes a general two-stage procedure for building a trinomial lattice

to represent a MR process for valuation of an option on a single underlying state variable.

For trinomial lattice the state variable can move up by a multiple u, down by a multiple

d or remain unchanged represented by na Since MR processes tend to move back to a

mean when disturbed, the trinomial lattice has three kinds of branching. Depending on

the current value of the state variable the next period movements can follow one of the

three branching patterns ((na, d, d), {u, na, d), {u, u, na) with associated probabilities. The

parameters of the lattice are determined by matching the moments of the trinomial and

MR processes. The procedure can be adapted for most forms of the MR process. Details

of the procedure can be found in Hull (2003).

The Multinomial Lattice Model for Two Underlying Correlated Stochastic Assets

When the problem is to find the value of an option on two underlying assets with

values that follow the GBM processes

dxC = pU,xldt +a~xldZ i = 1, 2 (3 -42)

which are correlated with instantaneous correlation coefficient given by p ( i.e., they

have a joint lognormal distribution) the contingent claim PDE has the form











12 1F' 2ea FI2 F22 3 (3-43)

+[r-32 2Fx2+F, rF+7t=0

A multinomial lattice approach is used to value such options, also called rainbow

options. The development of the multinomial process for the correlated assets is similar

to that described for the binomial lattice with a single underlying asset. The parameters of

the multinomial lattice are derived by matching the moments of the underlying asset

value processes. Hull (2003) discusses alternate lattice parameterization methods

developed for the multinomial lattice valuation approach. This study uses the method

discussed in Hull and White (1988). At each node on the lattice the assets can move

jointly to four states in the next period. The resulting parameter values for a three

dimensional lattice are summarized in Table 3-1.

Table 3-1. Parameter values for a three dimensional lattice

Period 1 state Risk neutral probability

ugu2 0.25(1+ p)
uz,d2 0.25(1- p)
dz,u2 0.25(1- p)
dz, d2 0.25(1+ p)


Here

uI =e 2 (3-44)


d. = e 2~ (3-45)

represent, respectively, the constant up and down movement multiplicands for asseti .

Parameter o, represents the volatility and 3, represents the dividend/convenience yield of

asseti while r represents the risk-free rate and A the size of the discrete time step















CHAPTER 4
APPLICATION OF THE CONTINTGENT CLAIMS MODEL

Who is the Pulpwood Farmer?

Before applying the CC model to the pulpwood farmer' s dilemma, it is necessary to

establish a mathematical description of a pulpwood farmer. For a commercial timber

production enterprise, the choice of timber products) to be produced (or rotation length

chosen) is guided by the prevailing and expected future timber market prices amongst

other things. The following discussion describes the role of relative timber product prices

in this decision.

A slash pine stand will produce multiple timber products over its life. For products

that are principally differentiated by log diameter, the early part of the rotation produces

the lowest diameter products like pulpwood. As the rotation progresses the trees gain in

diameter resulting in production of higher diameter products like sawtimber. Since

individual tree growth rates vary there is no en bloc transition of the stand from the lower

to a higher diameter product, but rather, for most part of the merchantable timber yielding

rotation ages the stand would contain a mix of products with the mix changing in favor of

the higher diameter products with increasing rotation age.

The average pine stumpage price data series reported by Timber Mart South (TMS)

for different timber products reveal that on an average the large diameter products garner

prices that are significantly higher than lower diameter product prices (Table 4-1). This

implies that the value of merchantable timber in the stand increases sharply with rotation

age from the combined effects of larger merchantable yields and increasing proportion of










higher diameter timber. More important, it also implies that short rotation farming may

be difficult to justify using the TMS reported prices.

Table 4-1. Florida statewide nominal pine stumpage average product price difference and
average relative prices (1980-2005)


Timber Products Average absolute Average relative
price difference prices
($/Ton)


Sawtimber vs. CNS 7.09 1.35
CNS vs. Pulpwood 9.79 1.99

Source: Timber Mart-South

In general, the cultivation of early rotation products is differentiable from that of

the late rotation products by the silvicultural choices. High density planting and absence

of pre-commercial thinnings are some choices that could characterize the cultivation of

pulpwood. For slash pine, the decision to plant dense and not resort to pre-commercial

thinnings limits the stand owner' s choices with respect to switching to higher diameter

product farming by prolonging the rotation.

For a general slash pine stand with two products pulpwoodd and sawtimber) the

results of price differentiation on the optimal Faustmann rotation age are shown in Table

4-2. The illustration uses a year 0 establishment cost of $120/acre, no intermediate cash

flows, a 5% constant annual discount rate, a cutover site index of 60 and 600 surviving

trees per acre (tpa) at age 2 with the Pienaar and Rheney (1995) slash pine growth and

yield equations. Pulpwood was defined as merchantable timber from trees with minimum

diameter at breast height (dbh) of 4 inches up to a diameter 2 inches outside bark and

sawtimber as trees with minimum dbh 8 inches to 6 inches outside bark. The

undifferentiated single timber product price was assumed $10/ton.










Table 4-2. The effect of timber product price differentiation on optimal Faustmann
rotation

Price difference Optimal rotation age
Absolute Relative
$/Ton Years

0 1.0 21
5 1.5 23
10 2.0 25
20 3.0 27
30 4.0 28


Similarly, Table 4-3 shows that it is the relative product prices (for the purpose of

this study, relative product price was defined as the price of the late rotation product

expressed as a proportion of the price of the early rotation product) that are important to

the determination of the optimal rotation changes. Table 4-3 maintains the absolute

increments while changing the size of the relative increments. For this illustration the

initial common timber product market price was assumed to be $20/ton.

Table 4-3. The effect of timber product relative prices on optimal Faustmann rotation

Price difference Optimal rotation age
Absolute Relative
$/Ton Years

0 1.0 20
5 1.25 21
10 1.50 23
20 2.00 24
30 2.50 26


Economic theory has it that relative pricing of goods is an important market signal

which allows the efficient allocation of resources. In the context of the timber stumpage

markets, relative product pricing serves as a signal to the timber producers to produce

(more/less of) one or the other timber product. In order to induce producers to increase










the production of late rotation products (which involve greater investment and/or risk) the

market must offer a higher relative price.

Since relative prices are not constant, an investment decision based on these prices

must consider an average or mean of relative prices over an appropriate period of time. A

timber land owner who bases his investment decision on the average relative prices

deduced from the prices reported by TMS would never choose the lower rotation ages

associated with pulpwood farming (19-25 years for slash pine from Yin et al. 1998). Yet

pulpwood farming is chosen by substantial numbers of timber land owners. Certainly, the

average relative price of products could not be the only reason for choosing the pulpwood

rotation. The practice of pulpwood farming with slash pine could be the result of several

considerations. Short rotations are attractive in themselves for the early realization of

timber sale revenues (capital constraint consideration). Other considerations like earning

regular income from the sale of pine straw in denser stands with no thinnings also

influence the choice. However, the alternate considerations do not diminish the

importance of relative product prices. The stumpage prices applicable to a particular

stand can be vastly different from the average prices reported by TMS. Some stands can

experience greater relative prices at the same point in time than others. This varying

relative prices experienced by stands can be easily explained by the nature of timber

markets. Once the maximum FOB price that a timber purchaser can offer is determined

for a period of time, the stumpage price applicable to prospective suppliers is determined

by the cost of harvesting and transporting the timber to the location of the purchaser' s

consumption/storage facility. From the average difference between TMS reported FOB

and stumpage prices, it can be seen that these costs form a very high (as much as 2/3 for










pulpwood) portion of the FOB price. At a point in time harvesting costs may vary little

from one stand location to another in a region but the transportation costs can vary

significantly. For a pulpwood stand located close to a purchaser (pulp mill) the relative

product prices would always be lower than those for other distant stands (say with respect

to a sawtimber purchaser in the region), justifying the pulpwood farming decision. This

argument implies that a significant number of the stands located close to pulp mills would

be choosing pulpwood farming and this should be empirically borne out. It also implies

that once multiple products are considered there can be no single Faustmann rotation age

that suits all even-aged single same specie stands even if their site quality was the same;

rather, there would be a continuum of optimal rotation ages depending on the average

relative stumpage prices applicable for the stand.

For the present analysis it was assumed that the slash pine pulpwood plantation was

located close to a pulpwood purchaser (who was not expected to stop operations)

resulting in experiencing low average (long run) relative prices. For sufficiently small

average relative prices it may also justify treating the entire merchantable timber output,

irrespective of diameter size, as pulpwood.

Despite the location advantage, in the shorter run, a stand would still experience

wide differences in relative product prices from fluctuating market conditions. In the

present conditions where pulpwood prices have been depressed for several years while

other products have fared relatively better, there is a market signal in favor of higher

diameter products to all stands irrespective of location. Therefore, the pulpwood stand

under analysis would be experiencing higher than normal relative prices. This situation










was analyzed as a multiproduct option problem though with modest relative prices as

compared to TMS reported prices.

The Return to Land in Timber Stand Investments

This section deals with the calculation of return to timberland. Land serves as a

store of value as well as a factor of production. As a factor of production used for the

timber stand investment, land must earn a return appropriate to the investment. For the

pulpwood farmer' s harvest problem land rent is a cost that will be incurred if the option

to wait is chosen. In the contingent claim analysis the land rent is modeled as a parameter

observed by the decision maker and hence having a known present value. If land is not

owned but rented/leased the explicit portion of this return is in the form of rent charged

by the renter/leaser. However, whether explicit or implicit, there is very little useful data

available on either timber land values or lease/rent values. Most decision makers do not

have access to a reliable estimate of even the present value of their timber land. This

makes it necessary to determine the appropriate return to be charged to land for the

purpose of the analysis. The following discussion uses the term 'timberland value' to

refer to the value of the bare land, unless it is specifically stated otherwise.

In its report on large timberland transactions in the US, the TMS newsletter (2005)

reports a weighted average transaction price in 2005 for the southern US of $1160/acre.

Smaller timberland transactions at $2000/acre or higher in Florida are routinely reported.

A part of these valuations must arise from the value of the land itself while some of the

balance could be for the standing trees (if any). Recent literature discusses other

important sources of the valuation like high non-timber values in the form of leisure and

recreation values etc. and expectations of future demand for alternative higher uses.

Aronsson and Carlen (2000) studies empirical forest land price formation and notes that









non-timber services, amongst other reasons, may explain the divergence of the valuation

from present value of future timber sale incomes. Wear and Newman (2004) discuss the

high timber land values in the context of using empirical timberland prices to predict

migration of forest land to alternate uses. Zhang et al. (2005) look at the phenomena of

timber land fragmentation or 'retailization' through sales to purchasers looking for

aesthetic/recreation values and its implications for forestry.

Since this analysis assumes that timber sales is the only maj or source of value the

appropriate timberland value is the value of bare land the present best use of which is

timber farming and for which non-timber values are insignificant. Assuming that the

market price of such timberland could be observed, the question is: Can this value be

used for the purpose of analysis? Is information on the traded price of bare timber land

appropriate for analysis?

Chang (1998) has proposed a modified version of the Faustmann model suggesting

that empirical land values could be used with the Faustmann optimality condition to

determine optimal rotations. Chang (1998) discusses a generalized Faustmann formula

that allows for changing parameters (stumpage price, stand growth function, regeneration

costs and interest rate) from rotation to rotation. The form of the optimal condition

derived by him is

8Rk (Tk >
S= BR(Rk k)+ BLEYk+1 (4-1)


Here, R(T) is the net revenue from a clear-cut sale of an even aged timber stand at

the optimal rotation age T and 6 is the required rate of return. The subscript k refers to

the rotation. The condition is interpreted to mean that instead of the constant LEV of the

standard Faustmann condition, the discounted value of succeeding harvest net revenues










(LEYk+1) must be substituted. Chang (1998) interprets this to mean that the market value

of bare land existing at the time of taking the harvest decision can replace the standard

constant Faustmann LEV. But, if the observed land value is very high as compared to the

LEV the RHS of the equation increases significantly, resulting in a drastic lowering of

the optimal rotation age T In the state of Florida, which is experiencing high rates of

urbanization, it is not unusual to find timber land valued at several multiples of the LEV.

Failure to account for non-market values like aesthetic or recreational values in the model

alone may not explain the failure to observe the rotation shortening effect. Klemperer and

Farkas (2001) discuss this effect of using empirical land values while using Chang' s

(1998) version of the Faustmann model.

By definition, the value of any asset is the discounted value of net surpluses that it

is expected to provide over its economic lifetime in its best use. This suggests that the

market value of land may be differentiable into two parts. One part of the market value is

derived from the current best use and the other is the speculative or expected future best

use (Castle and Hoch 1982). This means that the present market value of timber land, if

known, does not provide information on the value in current use without the separation of

the speculative value component.

The critical fact is that the land rent chargeable to current best use cannot exceed

the expected net surplus in current best use. No investor would pay a land rent higher

than the net surplus he expects to earn by putting it to use. Using empirical land values

could result in overcharging rents as the land values may be inflated by the speculative

value component.









This gives rise to the question: What about the opportunity cost to the speculative

component of land value? Does the landowner lose on that account? The answer is that if

a parcel of land is being held by the landowner despite its current market value being

higher than its valuation in current best use, an investment or speculation motive can be

ascribed to the landowner. The landowner treats the land not only as a productive factor

in the timber stand investment but also as a speculative asset. The landowner could earn a

capital gain over and above the value of future rents in the current best use by selling the

land in such a market. If the investor chooses to hold the land, it is because he expects to

profit from doing so. And this profit is in the form of expected capital appreciation. It is

this expected capital appreciation that compensates the landowner for the opportunity

cost on the speculative component of land value

A formal derivation of this argument follows from economic theory. According to

the economic theory of capital, in a competitive equilibrium, an asset holder will require

compensation for the opportunity cost on the current market value of a capital asset plus

the depreciation cost for allowing the use of his asset (Nicholson 2002). Representing the

present market price of the capital asset by P, the required compensation v will be

v = P(6 + d) (4-2)

where 6 is the percent opportunity cost and d is the assumed proportional depreciation on

the asset value. When the asset market value is not constant over time the required

compensation will be a function of time v(t) The present value of the asset would equal

the discounted value of future compensation incomes. At the present time t the present

discounted value (PDV) of the compensation received at time s (t < s) would be









v(s)es'"t'" and the present discounted value of all future compensation incomes would




PD'LV =P(t) =~ Iy(s)e- "(" 'ds -e"' my(s)e ""ds (4-3)

Differentiating P(t) with respect to t and ignoring depreciation we have


dP(t) -= Be' v(s)e-"d s -e" v(t)e -'= OP( t) -v(t) (4-4)


Therefore,

dP(t)
v(t) = OP(t) (4-5)


So, the required compensation income at any time is equal to the opportunity cost

on the current market value of the asset less the expected change in the market value of

the asset. The interpretation is that the 'fair' or competitive compensation for leasing an

asset consist of both the interest cost as well as the expected change in the value of the

asset. If the expected change in the value is positive the rental charges are decreased to

that extent since the value appreciation compensates the asset owner for a part of the

interest cost.

The net compensation v(t) is the opportunity cost for the asset value in its current

best use. It cannot be more than this cost as prospective renters cannot afford to pay more

as already argued. It cannot be less because a lower charge would transfer a surplus to the

renter attracting competition amongst renters.

Therefore, to find the amount to be appropriated as return to timberland it is

required that its value in current (best) use be determined and then the return would be

given by the opportunity cost of holding the land in its current (best) use.









The static Faustmann framework determines the timber land value or Land

Expectation Value (LEV) as the present value of net harvest revenues arising from

infinite identical rotations in timber farming use. In contrast, in the stochastic framework,

the ability to actively manage the investment adds an option value which must be

incorporated in the valuation method. As argued and shown by Plantinga (1998), Insley

(2002, 2005), and Hughes (2000) a price responsive harvest strategy adds a significant

option value to the investment.

In this study the land value was determined within the CC analysis assuming that

timber farming was the current best use. The parameters for the valuation are the current

values of timber price and plantation establishment cost as well as annual maintenance

costs. In the risk neutral analysis the current risk-free rate serves as the discount rate. The

infinite identical rotations methodology was used to capture the tradeoff with future

incomes meaning that the net expected surplus value over the first rotation was used as

the expected average value for future rotations. This may not effect the land value

significantly since, as observed by Bright and Price (2000), the present value of net

surplus in the first rotation forms most of (>80%) the estimated timberland value when

calculated in this way, for a sufficiently long rotation and high discount rate. Therefore,

the land value in current use can be estimated with information available to the decision

maker. And the land rent is the opportunity cost of this value. The mathematical

formulation of the land value estimation problem is given by Equation 4-6.



L V = max (4-6)
Ly =maxI E~[PIP,])(-e-rt C


Here,











L V = Present value of land
Eq = Risk neutral expectation operator
P, = Stochastic timber price at t period from present time 0
Q~t) = Deterministic merchantable timber yield function (of rotation age t)
at = Annual recurring plantation administration expenses treated as risk-free asset
Co = Value of plantation establishment expenses to be incurred today (at year 0)
r = Present risk-free interest rate assumed constant in future

Then, the present value of estimated land rent is rL V

There are some points to note about the above arguments and methodology

outlined for determining the land value in current best use. First, it is implied that the rent

value is calculated afresh by the decision maker every period. This is empirically true for

shorter duration uses like agriculture farming and there is no reason why it should not be

so for timber farming if decision makers are efficient information processors as normally

assumed and information is easily and freely accessible. If market information on

comparable land rents was available, it would be stochastic and the decision maker would

utilize the new information available every period for decision making.

It is also important that a stochastic rent value calculated as argued above captures

and transfers fresh information about the expected future to the decision making process.

That is, if the estimated land/rent value is high, it will increase the cost of rotation

extension and vice versa. For example, if the stand owner learns of a demonstrable

technological advance improving the financial returns to stand investments, in the midst

of the rotation, the stand owner will seek to apply the technology to the present rotation,

thereby adopting the 'best use'. However, if the improved technology cannot be applied

to the current rotation then there should be pressure on the decision maker to shorten the

present rotation so that the improved technology could be applied to the next rotation.









There is no empirical evidence known to support this result regarding timberland owner

behavior but it can be argued that timberland owners never have free and easy access to

the necessary information. It is also important to note that market prices of timberland

themselves provide no valuable input to the stand decision making process but rather it is

the value in current best use that is relevant. Thus, in periods of speculative inflation of

land prices one may not expect to observe any appreciable changes in stand decision

making behavior. It is the direction of changes in real input costs and output values which

result in changes in land rent.

Second, it is implied that the rent value is a function of the current timber pricess.

However, it need not be perfectly correlated with timber prices(s) since other parameters

of the valuation (the costs and discount rates) would be expected to follow (largely)

independent stochastic processes.

Third, the method outlined for estimation of land rent provides an estimate for a

single period i.e., for the present period only. Ideally, the rent value should be modeled as

a stochastic variable. But that would require information regarding the stochastic process

defining the plantation expenses (or non-timber sources of cash flow) and discount rate.

In the absence of data on the stochastic process for the other flows, in the following

analysis, land rent was assumed to behave like a risk-free asset.

On the Convenience Yield and the Timber Stand Investment

To solve the harvesting problem using the lattice approach the constant volatility of

the underlying variable is estimated from historical price data. The risk-free rate is

estimated from yields on treasury bills of matching maturity. However, the estimation of

the convenience yield poses a problem. The concept of convenience yield, as it is

popularly interpreted, was first proposed by Working (1948, 1949) in a study of









commodity futures markets. The phenomena of "prices of deferred futures....below that

of the near futures" (Working 1948, p. 1) was labeled an inverse carrying charge. The

carrying charge or storage cost is the cost of physically holding an asset over a period.

The concept can be illustrated as follows: ignoring physical storage costs, the arbitrage

free forward price F for future delivery of a commodity is determined by the relation

Poe't where P is the current unit price of the commodity, r is the borrowing/lending

rate while t is the period of the contract. Therefore, the forward price should be

proportional to the length of the contract. The inverse carrying charge or convenience

yield discussed by Working (1948, 1949) is said to accrue to the contract writer when the

no arbitrage relation does not hold for some contract lengths and F
an opportunity for arbitrage exists, arbitrageurs are unable to take advantage as nobody

that is holding the commodity in inventory is willing to lend the commodity for shorting.

Inventory holder may be unwilling to lend the commodity when markets are tight

(Luenberger, 1998) i.e., supply shortage is anticipated. Brennan (1991) defines

convenience yield of a commodity as:

...the flow of services which accrue to the owner of a physical inventory but not to
the owner of a contract for future delivery. ....the owner of the physical commodity
is able to determine where it will be stored and when to liquidate the inventory.
Recognizing the time lost and the costs incurred in ordering and transporting a
commodity from one location to another, the marginal convenience yield includes
both the reduction in costs of acquiring inventory and the value of being able to
profit from temporary local shortages of the commodity through ownership of a
larger inventory. The profit may arise from either local price variations or from the
ability to maintain a production process despite local shortages of a raw material.
(Brennan 1991, p.33-34)

The convenience yield is not constant but would vary with the gross inventory of

the commodity in question, amongst other things.









If there exists a futures market for the commodity then the futures prices represent

the risk neutral expected values of the commodity. The risk neutral drift pu(t) which will

be a function of time since the convenience yield 3(t) and forward risk free rate r(t) are

empirically stochastic, can be calculated from the futures prices as (Hull 2003)

8[1n~t) F=+1
p~lt) = ]d =I In 1Ft (4-7)


Here, F(t) is the futures price at time t.

In the absence of a futures market, theoretically it should be possible to estimate the

convenience yield by comparing with equilibrium returns on an investment asset that

spans the commodity's risk (replicating portfolio).As discussed by McDonald and Siegel

(1985), the difference between the equilibrium rate of return on a financial asset that

shares the same covariance as the asset and expected rate of return on the asset will

yield 3 But, empirically, such an asset is difficult to locate or construct from existing

traded assets.

Similarly, we could estimate r 3 from the equivalent pu Alpo Usually, the

Capital Asset Pricing model (CAPM) for timber stands is estimated by regressing excess

returns on the historical timber price against the excess returns on the market portfolio.

Thus, this methodology suffers from the failure to incorporate the convenience yield in

total returns on timber stands. Using the estimated ilp by this method will only yield

pu Alpo = r i.e., a 3 value of zero. To the best of this authors' knowledge no method

for estimating convenience yield for timber is available in published literature. Therefore,

this study proceeds by assuming that the convenience yield 3 = 0 and pu Alpo = r The

results are tested for sensitivity to different levels of constant 6 .









Dynamics of the Price Process

Modeling the empirical price process is the key to the development and results of

the real options analysis. Beginning with Washburn and Binkley (1990a) there has been

debate over whether the empirical stumpage price returns process is stationary (mean-

reverting) or non-stationary (random walk). The debate has remained inconclusive due to

the conflicting evidence on the distorting effect of period averaging on prices. Working

(1960) was the first to show that the first differences of a period averaged random chain

would exhibit first order serial correlation of the magnitude of 0.25 (approximated as the

number of regularly spaced observations in the averaged period increased). Washburn

and Binkley (1990a) found consistent negative correlation at the first lag for several

quarterly and annual averaged stumpage price series though most were less than 0.25 and

statistically significant only for prices in one case.

On the other hand, Haight and Holmes (1991) have provided heuristic proof to the

effect that a stationary first order autoregressive process, when averaged over a period,

would behave like a random walk as the size of the averaging period was increased. They

used this proof to explain away the observed non-stationarity in the quarterly averaged

stumpage prices.

The stationarity of the price process has implications for the efficiency of stumpage

markets. "A market in which prices always "fully reflect" available information is called

"efficient"" (Fama 1970, p.3 83). Utilizing the expected rate of return format, market

efficiency is described as


E Pr ; .=,,i = +Ery : O $ (4-8)










Here, E is the expectation operator, P~, is the price of security j at time t, P,,r I its


random price at time t +1 with intermediate cash flows reinvested, r ,~t~l is the random


P, ri P
one-period percent rate of return .'' O, represents the information set assumed
P~,

to be fully reflected in the price at t .

The information set O, is further characterized according to the form of efficiency

implied i.e., weak form efficiency which is limited to the historical data set, semi-strong

form efficiency which includes other publicly available information and strong form

efficiency that also includes the privately available information. As Fama (1970)

discusses, the hypothesis that asset prices at any point fully reflect all available

information is extreme. It is more common to use historical data to test prices for weak

form efficiency in support of the random walk model of prices.

Washburn and Binkley (1990a) tested for the weak form efficiency using the

equilibrium model of expected returns with alternate forms of Sharpe' s (1963) single-

index market models. Ex-post returns to stumpage were regressed on a stock market

index and an inflation index. The residuals from the regressions were then tested for

serial correlation the presence of which would lead to rej section of the weak form

efficiency hypothesis. Since these tests required the assumption of a normal distribution

for the residuals, this was tested using the higher moments skewnesss and kurtosis). The

non-parametric turning points test was also conducted as an alternate test for serial

dependence. They found evidence of stationarity in returns generated from monthly

averaged data but returns generated from quarterly and annually averaged data displayed









non-stationarity. Significantly, they did not find evidence to support the normal

distribution assumption of the residuals.

Haight and Holmes (1991) used an Augmented Dickey-Fuller test and found

stationarity in instantaneous returns on monthly and quarterly spot stumpage prices and

non-stationarity in instantaneous returns on quarterly averaged stumpage prices.

Hultkrantz (1993) contended that the stationarity found in returns generated from

monthly averaged price series by Washburn and Binkley (1990a) could be consistent with

market efficiency when producers were risk averse. He used a panel data approach to

Dickey-Fuller tests and found that southern stumpage prices were stationary. Washburn

and Binkley (1993) in reply argued that the results of Hultkrantz' s analysis were by and

large similar to their analysis and point out that if Haight and Holmes (1991) proof of the

behavior of averaged prices is considered, then both (Hultkrantz 1993 and Washburn &

Binkley 1990) their analyses could be biased away from rej section of the weak form

market efficiency. Yin and Newman (1996) used the Augmented Dickey-Fuller and

arrived at conclusions similar to Hultkrantz' s (1993).

Gj olberg and Guttormsen (2002) applied the variance ratio test to timber prices to

check the null hypothesis of a random walk for the instantaneous returns. Their tests

could not rej ect the random walk hypothesis in the shorter periods (1 month and 1 year)

but over longer horizons, they found evidence of mean reversion.

Prestemon (2003) found that most southern pine stumpage price series returns were

non-stationary. He noted that tests of time series using alternate procedures may not agree

regarding stationarity or market informational efficiency as time series of commodity

asset prices may not be martingales.









McGough et al. (2004) argue that a first order autoregressive process for timber

prices is consistent with efficiency in the timber markets. They advocate the use of

complex models (VARMA) that include dynamics of the timber inventory while noting

that such models would be difficult to estimate and apply to harvesting problems.

In summary, in the absence of better data and models or stronger tests, it is difficult

to conclusively establish the efficiency or otherwise of stumpage markets and or choose

between the random walk or autoregressive models. This study considered both, the

stationary and non-stationary models, for the price process alternately.

Modeling the Price Process

Two alternate models for the stochastic price process were applied to the real

options model. The first model is the Geometric Brownian motion which a form of the

random walk process that incorporates a drift and conforms with the efficient market

hypothesis. Expressed mathematically it is

dP = puPdt + oPd: (4-9)

Here,

P =Price of the asset at time t
pu = Constant drift
a = Constant volatility
dz = Increment of a Weiner process

Geometric Brownian motion processes tend to wander far away from their

starting points. This may be realistic for some economic variables like investment asset

prices. It is argued that commodity prices (Schwartz 1997) must be related to their long-

run marginal cost of production. Such asset prices are modeled by Mean Reverting

processes, which is the second model used for the stochastic process in this analysis.

While in the short run the price of a commodity may fluctuate randomly, in the long run










they are drawn back to their marginal cost of production. The Ornstein-Uhlenbeck

process is a simple form of the MR process expressed as


dP= 9 P- Pdt+ rd (4-10)


Here,

r = Coefficient of reversion

P = Mean or normal level ofP

The r is interpreted as the speed of reversion. Higher values of r correspond to


faster mean reversion. P is the level to which P tends to revert. P may be the long run

marginal cost of production.. The expected change in P depends on the difference


between P and P. If P is greater (less) than P, it is more likely to fall (rise) over the next

short interval of time. Hence, although satisfying the Markov property, the process does

not have independent increments.

The Weiner process in discrete time is expressed as

z, q,= Z,+ e,~ (4-11)

Here,

E,= Realization of a Normal Random variable with mean 0 variance 1 and
Cov(e,,e,,)= 0 for jf0

In continuous time, the process is diz, = e,Jd A Weiner process z, is a random

walk in continuous time with the properties (Luenberger 1998)

i. For any s < t the quantity z(t) z(s) is a normal random variable with mean zero and
vaniancet -s .

ii. For any 0 < t, < t, < t, < t4 the random variables z(t,) z(t) land z(t4) 3~, r
uncorrelated.









iii. z(t,) = with probability 1.

The Geometric Brownian Motion Process

Applying Ito's lemma, the Geometric Brownian motion (GBM) process can be

expressed in logarithmic form as


dn =pi o1\dt +odz (4-12)

In discrete logarithmic form the equation becomes


IIn'~:.l g n t + odz (4-13)

Thus, the log-difference or the instantaneous rate of price change is normally

di stributed.

In order to model the GBM process an estimate of the volatility was required.

Following Tsay (2002), let r, = In P, In P Then, r, is normally distributed with mean


pY a At and vrianac a At, wher e Atii is inite timeinterval. If s denotes the

sample standard deviation i.e.,


i; r,- r
s t= g1 (4-14)


then


a = (4-15)

Here a denotes the estimated values of a from the data. For the nominal F.O.B. and

stumpage statewide pulpwood quarterly price data for Florida, using the above

methodology we obtain the estimates listed in Table 3-4.









However, as the TMS data is available in period average form while the GBM

process models the behavior of spot prices, it is necessary to account for any distortion to

the statistical properties of the data from averaging. Working (1960) has demonstrated

that to an approximation, the variance of rates of change calculated from arithmetic


averages of n consecutive regular spaced values of a random chain will be of the

variance of first difference of correspondingly positioned terms in the unaveraged chain,

as n increases. The prices reported by TMS are calculated as an arithmetic average of all

reported prices in a quarter. As discussed by Washburn and Binkley (1990b), the price

averages will be unbiased estimates of the arithmetic mean of prices at any n regular

intervals within the period so long as the likelihood of a timber sale occurring and the

expected transaction size are constant throughout each period.

Making the necessary correction to the estimated variance we obtain the revised

estimate of the variance listed in Table 4-4.

Table 4-4. Estimated GBM process parameter values for Florida statewide nominal
quarterly average pulpwood prices

Estimated Parameter FOB Stumpage

Uncorrected Values Standard Deviation 0.10 0.24
Corrected Values Standard Deviation 0.12 0.29


It may be noted that the calculated standard deviation for the F.O.B. price was

significantly lower than the standard deviation of the stumpage price. One possible

explanation is that pulp mills revise their mill delivered prices relatively infrequently,

whether they are gate purchase prices or supplier contracted prices. It is also possible that

while gate purchase prices are public knowledge, mill delivered price of pulpwood

purchased from other sources may be incompletely reported due to mill concerns with









strategic competitive disadvantage from revealing prices. On the other hand harvesting

and transportation costs change drastically from one stand to another, resulting in higher

volatility of reported stumpage prices. The stumpage prices reported are not the prices

experienced by a particular stand or a common price experienced by all stands but prices

experienced by different stands that reported selling timber in the period. The harvesting

and transportation costs are themselves volatile and likely imperfectly correlated with

FOB prices but it is possible that they do not account for the entire difference in the

reported standard deviations. Since stand owners experience the stumpage price and not

the FOB price, in the absence of data on volatility of harvesting and transportation costs,

this study uses the estimated standard deviation of reported stumpage prices to replace

the estimated volatility of F.O.B. prices while treating the harvesting and transportation

costs as non-stochastic. To account for the possibility of overestimation of timber price

variance the analysis was subj ected to tests of sensitivity to price volatility.

Statistical Tests of the Geometric Brownian Motion Model

The GBM process in discrete logarithmic form is a discrete random walk with drift

i.e., it has the general form

y, = y,/ + a + E, where E, I N(0, a At) and Clov(e,E, ) = 0 for j f 0 (4-16)

The process is clearly non-stationary with a unit root. But if we take the first

difference we obtain a stationary process

Ay, = a + E, (4- 17)

The first difference process has mean a and variance o At Further, the

covariance Cov(Ay,, Ay,,) = 0 for s+ 0.









To see how well the empirical data fits the GBM model, the sample autocorrelation

function (ACF) at several lags was calculated and plotted for the difference logarithmic

form of the price data series. The sample ACF [p(h)] at lag h was calculated using the

formula




p(h)= n -n


tZ =1


For large n, the sample autocorrelations of an independent identically distributed


(iid) sequence with finite variance are approximately iid with distribution N(0, 1


(Brockwell and Davis 2003). Hence, for the iid sequence, about 95% of the sample

1.96
autocorrelations should fall between the bounds+ .


For the GBM process the instantaneous rate of price change r, = In P, In P, is





zero for all he 0 If the empirical price data is modeled by the GBM process, then the

sequence tr, J should be white noise i.e., it should be a sequence of uncorrelated random

variables. A plot of the sample autocorrelations for the instantaneous rate of price

changes of the reported nominal pulpwood statewide stumpage prices along with the 95%

confidence intervals are presented in Figure 4-1, plotted using the ITSM 2000 statistical

software (Brockwell and Davis 2003).










1 OO Sample ACF









80-

40- 2 5 O364








Figure 4-1. Sample autocorrelation function plot for nominal Florida statewide pulpwood
stumpage instantaneous rate of price changes

The dashed lines on either side of the centre plot the 95% confidence interval. If

the sequence is stationary, for the 40 lags plotted, 2 or less ACF's should fall beyond the

95% confidence bounds. For the stumpage price series, no more than 1 ACF beyond lag 0

fell outside the 95% confidence bounds. Significantly, as proved by Working (1960), the

ACF at lag 1 for stumpage price instantaneous rate of change sequence was approx. 0.25


( p(1) = -0.27 ). This could be the effect of the averaging process.


To check if the (r \ sequennncwa Gaussia i-e.,;~ if al of~1 its+E joint distributions are


normal the Jarque-Bera test was used. The Jarque-Bera test statistic is given by

(Brockwell and Davis 2003)



n +m distributed asymptotically as X (2) (4-19)












if (Yt>, } I ID N,,i~ cr)whr m


The results of the Jarque-Bera test applied to the stumpage prices, given in Table 4-

5, indicate that the normality hypothesis was weakly supported by the empirical data at

the 5% level of significance.

Table 4-5. Results of Jarque-Bera test applied to GBM model for Florida statewide
nominal quarterly average pulpwood stumpage prices

Test Value Stumpage

Jarque-Bera Test Statistic 6.0984
P-value 0.0474


The ACF test indicates that the GBM process can be used to model the empirical

data. However, this does not exclude the possibility of the true price process having a

non-constant drift and/or variance. Lutz (1999) tested stumpage price series for constant

variance. He found that the variance for stumpage price series examined was not constant

for the early parts of the series i.e., up to 1920. From 1920 onwards, the examined series

were found to display constant variance.

Even if stumpage price data is heteroskedastic, it was implicitly assumed that the

logarithmic transformation rendered the data homescedastic. The test results also did not

exclude the possibility of an alternate model providing a better fit. On the basis of the

ACF test it was only possible to conclude that there was insufficient evidence to rej ect

the GBM model.

The Mean Reverting Process

The simple MR process is given by













Hence, Pis normally distributed with E(P, | P, )= P+ -P e-t and


2r


Using the expected value and variance we can express P, as


1-e
2')9


41 ,= GP1P- + e, when (1-e") = 1 (4-20)


where E, IN 0,a2

The last equation provides a discrete time first order autoregressive equivalent of

the continuous time Ornstein-Uhlenbeck process. In order to estimate the parameters an

OLS regression of the form

p, 4F = a + bP + e, (4-21)

with a = yP and b = -r was run. Then, the estimated parameters are given by


P= -a = -b and a = o,


One problem with using this simple form of the MR process is that it allows

negative values for the stochastic variable. Plantinga (1998) justifies the choice of this

model for timber stumpage price by referring to the possibility of harvesting and

transporting costs exceeding the FOB timber price. In such a case the effective stumpage

price would be negative. However, it can be argued that the negative stumpage price









would still be bound by the harvesting and transportation costs i.e., if the FOB price were

zero, the negative stumpage price cannot be larger than the cost for harvesting and

removal. But the MR process described above is unbounded in the negative direction.

Hull (2003) describes an alternate log normal form of the MR process


dlni = 9 InP-Ing dtrcrd (4-22)

This model restricts the price process to positive values. Thus, In gF given InP, is



normally distributed with mean In P+~ In In Pu e-t and vanianceor 1 -e-2,) .The

deviations from the long-run mean are expected to decay following an exponential

decline. This analysis uses this form of the MR process to model the FOB prices. When

the harvesting and transportation costs are deducted from the stochastic values of the

FOB price the magnitude of resulting negative stumpage price is restricted to these costs.

However, adopting Equation 4-21 for estimation of P implies that the mean to


which the process reverts is constant over any period of time. Considering that P is

interpreted as the long run cost of production, over a short interval of a few days or

weeks, it may be feasible to assume that the value is constant. But when the analysis must

cover several years, this assumption is questionable. One common correction method

employed (Smith and McCardle 1999) is to regress the inflated values (present) of the

historical asset price, which yields an inflation adjusted estimate ofP It also implies that


for an analysis conducted in nominal terms, the future values of P must be inflated at an

estimated average inflation rate. The average rate of annual inflation computed from the

PPI (1921-2005) was approx.3.0%.










The possibility of a constant real or inflation adjusted nominal P for pulpwood

prices was corroborated by the historical performance of pulpwood prices over the 30

years or so of TMS reporting period as well as the RPA (2003) proj sections of future

performance. This phenomenon can be partly attributed to technological advances and in

some measure to adverse demand and supply movements. Considering that the other

parameter estimates are only marginally effected this analysis uses the inflation adjusted

parameter values.

To check for the effect of period averaging on the estimated parameter values,

simulation was carried out. The simulation revealed that regression of period averaged


data generated consistent estimates of P while cr was consistently underestimated by a

factor of approx. 0.67 or the Working' s correction. The result of regressing inflated past

values of the pulpwood prices using the Producers Price Index-All Commodities (PPI) on


estimated P and other parameters are listed in Table 4-6.

Table 4-6. Inflation adjusted regression and MR model parameter estimates


a b P r a*


Stumpage Price 0.2979 -0.1245 2.3922 0.1245 0.2230
Standard Error 0.3285 0.1278
FOB Price 0.5694 -0.1781 3.1963 0.1781 0.0945
Standrad Error 0.4338 0.1350

* Estimates corrected for period averaging effect.

Of particular importance are the reversion coefficient values. For both price series

the reversion coefficient values are low indicating that the annual price series exhibit low

or insignificant reversion behavior. The 'half life' of the MR process or the time it takes









10 0.5
to revert half way back to the long run mean, given by --, was approximately 2.6


years for stumpage price, illustrating the extremely slow reversion process. Regarding the

low values of mean reversion coefficients Dixit and Pindyck (1994) observe that this

seems to be the case for many economic variables and that it is usually difficult to rej ect

the random walk hypothesis using just 30 or 40 years data.

Secondly, the estimated variance for the FOB price process was sharply lower than

that for the stumpage price. Once again, this difference can be attributed to the stochastic

harvesting and transportation costs but may also partly be the result of the unsuitable

data. As in the GBM process case, the analysis was conducted by attributing the

stumpage price process variance to the FOB price process and using a non-stochastic

harvesting and transportation cost.

Finally, it must be noted that the lattice model for MR process used in this analysis

was based on the existence of futures markets for the commodity and hence knowledge of

futures prices, which represent the risk neutral expected future values. In the absence of


futures markets for timber, the value of the inflation adjusted estimated P was used. This

was justified for a long interval since mean reverting prices (and hence futures prices) are


expected to converge to P in the long run. However, in the short run this only serves as

an approximation.

Statistical Tests of the Mean Reverting Process Model

Examination of the stumpage price regression residuals shows first order serial

correlation (Figure 4-2) as shown by Working (1964).










Sample ACF












40- 2 5 0364


OO mpg Irc IR Ioe rersso I | 1a









Figre -2.Sampe atcrrlto fnto po o nominal Floridal statewie pulpwood tma pie

Tst ttsi tumpage priceMRmdlrgesorsius







Jarque-Bera Test Statistic 9.6299
P-value 0.0081



Instantaneous Correlation

In order to model the simultaneous stochastic evolution of two correlated stochastic


processes following the GBM, an estimate of the instantaneous correlation between the

two time series was required. The estimation of the instantaneous correlation of two


period averaged GBM processes is not effected by period averaging (Appendix). The

estimated instantaneous correlation for the TMS reported Florida statewide average


stumpage quarterly prices of pulpwood and chip-n-saw assuming GBM processes was

0.43.










The Data

Time series data on prices of the timber products was acquired from Timber Mart-

South (TMS). Price data for Florida extending from the last quarter of 1976 for pulpwood

and the first quarter of 1980 for chip-n-saw to the second quarter of 2005 are used for the

analysis. The stumpage price data are reported by Timber Mart-South as quarterly

average of final sale prices recorded in auctions for timber products in the reporting

region.

The data was used to represent spot timber prices in the analysis. However, due to

the nature of data generation, collection and reporting processes, the validity of the data

for this purpose is suspect. For example, the process starting from bidding for the timber

to removal of the timber from the stand is usually a few months long. This means that the

auction bid prices are a reflection of the bidder' s expectation regarding future prices

when the timber will actually reach the market, not the immediate price. Errors in

recording, approximations etc also undermine the data. Other shortcomings have been

discussed in various contexts above.

Harvesting and transportation cost was calculated using the difference between

reported F.O.B. and stumpage prices of timber. The appropriateness of this method is

questionable because of the time difference between auctions and actual movement of

timber from the stand to the market.

Growth and Yield Equations

Slash pine growth and yield equations developed by Pienaar and Rheney (1996) are

used. These equations for cutover forest land were developed using data from plantations

sites in Georgia. The average site index for the sites was 60 ft (at age 25). The equations

used in the analysis are







77


i. Expected Average dominant height (H in ft)

H = 1.3679S(1 e-C)7345dge 1 8()4 + (0.678Z, + 0.546Z2 + 1.395Z3 0.412Z Z )Age "e-C)(691Age

where


=Site Index
=1 if fertilized, zero otherwise
=1 if bedded, zero otherwise
=1 if herbicided, zero otherwise


N,e-)()(41(Age -Age
Age, and Age,


ii. Survival after the second growing season (in trees/acre) N,
where N, and N, are trees per acre surviving at
respectively (Age, > Age, ) .


iii. Basal area (B in ft2 /ce

B =e3 394-35 668 4e 1 336+6 2)5.4g () 366+3 155.4g

(0.557Z, + 0.436Z, + 2. 134Z3 0.3 54Z,Z )ag eOe- 09.4ge
where

Z, = 1 if burned, zero otherwise


iv. Stem Volume outside bark( V in ft3/ce

~~~r-( ) O82 17- 1 (1eg )16+"""~;S


v. Merchantable volume prediction (T in ft3ice
T38 15"


where


V 9, =per acre volume of trees with dbh>d inches to a merchantable diameter t inches
outside bark

D = quadratic mean dbh in inches =
0.005454N\/
tr = pl










The merchantable yield output from the growth and yield models is in units of ft3

outside bark/acre. To convert the yield to tons/acre conversion factors of 90 ft3/COrd and

2.68 tons/cord (Timber-Mart South) were used.

These equations were developed from experimental plantations reaching an age of

16 growing seasons. For this reason, their use for extrapolating growth and yield to

higher rotation ages is questionable (Yin et al.1998) and may not represent the true stand

growth. Nevertheless, for the purpose of this analysis, these equations are the best source

for modeling the growth and yield of slash pine.

Plantation Establishment Expenses

Average plantation establishment expenses for cutover land in the US South

reported by Dubois et al. (2003) were used. The relevant reported costs are listed in Table

4-8.

Table 4-8. Average per acre plantation establishment expenses for with a 800
seedlings/acre planting density

Expense Head 2002 2005

$/acre $/seedling $/acre $/seedling

Mechanical Site preparation* 166.50 195.82

Burning** 15.02 17.66

Planting cost* 49.99*** 58.79

Seedling cost 0.04 0.05


Total Cost for 800 seedlings/acre 280.00 329.21

* All Types ** Others *** Planting cost for average 602 seedlings/acre

The Producers Price Index-All Commodities was obtained from the Federal

Reserve Economic data and used to extrapolate the nominal plantation expense data










reported for 2002 to 2005. The index stood at 132.9 in December 2002 and rose to 156.3

by July 2005. For an acre planted with 800 seedlings the total planting cost under the

above listed expense heads in July 2005 was estimated at $329.21 or approx. $330/acre.

Risk-Free Rate of Return

The yield on Treasury bills with 1 year maturity (Federal Reserve Statistical

Release) was used as the estimated risk-free rate of return. The reported risk-free rate for

July 2005 was 3.64%.

The Model Summarized

The value of options available to the decision maker were analyzed using a CC

valuation procedure. The analysis also highlighted the form of the optimal strategy. The

following are the important points of the model

1. The model considers an even-aged mature (20 year age) slash pine pulpwood
plantation in 2005. Only revenues from sale of timber are considered significant for
the analysis. Since the analysis focuses on the pulpwood crop, the plantation was
assumed to have been planted dense (800 trees/acre initial planting density) with no
thinnings up to the present age. The plantation was assumed to be cutover with site
index 60 ft (rotation age 25). Site preparation activities assumed are mechanical site
preparation (shear/rake/pile) and burning only. A clear-cut harvest was considered for
the final harvest. When the thinning option was the subj ect of analysis only a single
thinning in the form of a row thinning that removes every third row of trees was
considered.

2. For a stand with the chosen initial planting density and site index on a cutover site,
the growth and yield equations produce a single product yield curve that peaks
approximately at age 43. Rotation age 43 was selected as the terminal age for the
options on the stand in this study. This terminal age was applied uniformly to all
models for comparability of results. Even though later stand products would have
later yield peaks, current empirical practices and unreliability of the yield curve for
higher rotation ages were arguments in favor of the lower terminal age.

3. Only the timber price (prices for multiple product analysis) was modeled as a
stochastic variable. It was assumed that the stand growth and yield models provide a
reliable forecast of the future merchantable timber yields.

4. The present values of other parameters of the valuation model like the risk-free
interest rate, the land rent and the intermediate expenditure/cash flows on plantation










were assumed known to the decision maker. Some intermediate cash flows could be
positive in the form of regular realizations of amenity values or sale of some
minor/non-timber products while others could be negative in the form of annual taxes
and overhead expenditures associated with maintenance activities. The basic analysis
assumes that the net result from a combination of both positive and negative cash
flows was a negative cash flow of $10/acre/year. For the purpose of consistency, all
intermediate cash flows are treated as occurring at the beginning of a period. This
arrangement does not effect the analysis since the intermediate cash flows are
assumed non-stochastic. Non-stochastic variables are unaffected by the expectation
operator but are effected by discounting. So, regardless of where they occur in the
period their value at the beginning of the period can be considered as the
appropriately discounted value. These known values were extrapolated like risk-free
variables i.e., with close to zero variance (and no correlation with the stochastic
variables). Harvesting and transportation costs per unit merchantable timber were
assumed constant i.e., the effect of economies of scale observed for older or larger
stands was ignored for want of data.

5. The unit FOB price of the timber product was modeled as the stochastic variable and
the unit harvesting and transportation costs deduced from empirical data served as the
strike price for the option on the stand. The estimated empirical values of variance for
the timber stumpage price were used to model the variance of the FOB price.

6. The GBM and MR models applied were assumed to have constant parameters i.e., the
drift and variance for the GBM model and the reversion coefficient and variance
parameters for the MR models were assumed constant.

7. Land rent was estimated for the stochastic price process using the CC valuation as
detailed earlier.

8. Taxes are not specifically treated in the analysis.

9. Ideally the term structure of interest rates should be used to model the risk-free rate.
For simplicity, a single constant risk-free interest rate was used instead.

10. An assumption was made that the pulpwood stand was located so as to experience
low/moderate relative timber product prices.

11. For the basic model the convenience yield was assumed to be zero. Sensitivity
analysis to consider the effect of positive values of the convenience yield was
conducted.

















CHAPTER 5
RESULTS AND DISCUSSION

A Single Product Stand and the Geometric Brownian Motion Price Process

In the following section the entire merchantable output of the stand at any rotation

age was treated as a single undifferentiated product, in this case pulpwood. As argued

earlier, this would be the case for a stand experiencing low relative timber product prices.

Figure 5-1 plots the per acre merchantable timber yield curve for a cutover slash pine

stand in Florida with the following site description and management history


140.00-


120.00


2 100.00


t 80.00

-*-Total Merchantable Wood

60.00


T 40.00


20.00


0.00

Rotation age (Years)



Figure 5-1. Total per acre merchantable yield curve for slash pine stand











* Site Index -60 (age 25)
* Site Preparation- Burning only
* Initial planting density- 800 Trees per acre
* Thinnings None

Parameter values used for the analysis are listed in Table 5-1.

Table 5-1. Parameter values used in analysis of harvest decision for single product stand
with GBM price process


Parameter Effective Date/ Unit Value
Period


FOB price II Qtr 2005 $/Ton 21.96
Stumpage price II Qtr 2005 $/Ton 7.42
Harvesting and transportation cost II Qtr 2005 $/Ton 14.54

Initial plantation expenses II Qtr 2005 $/acre 330.00
Estimated land rent II Qtr 2005 $/acre/year 34.00
Other annual expenses II Qtr 2005 $/acre/year 10.00

Estimated standard deviation 07-01-2005 Annual 0.29
of GBM price process
Risk free rate 07-01-2005 %//annum 3.64
Constant convenience yield Annual 0.00
Present age of stand 07-01-2005 Years 20.00


In order to value the option to postpone the clear-cut harvest the FOB price for

pulpwood was modeled as a stochastic variable following a GBM process with a constant

standard deviation of 0.29. A binomial lattice was constructed using Equations 3-28, 3-29

and 3-37 for this stochastic variable with a one year period. The backward recursive

option pricing procedure was then implemented to determine the option value. The

GAUSS Light version 5.0 (Aptech Systems, Inc.) software was used for finding

solutions.

The per acre pre-tax value of an immediate harvest and sale as pulpwood of the

entire merchantable yield at rotation age 20 at current stumpage price of $7.42/ton was











$567. The maximum or terminal rotation age considered was 43 years. At $966/acre or


$12.64/ton the calculated option value was higher than the value of immediate harvest.


Figure 5-2 plots the upper bounds of the stumpage prices for the harvest region or

the crossover price line. Since a discrete time approximation with large period values


(annual) was used continuity was sacrificed i.e., the reported values of crossover prices


display large jumps. The crossover line has also been smoothened to remove the

incongruities in the data recovered fr~om the discrete lattice structure.


200.00-

180.00

160.00

1 40.00

S120.00

S100.00 -m--Optimal cross-o\Rr prices

a. 80.00

S60.00

40.00

20.00

0.00
20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
Rotation Age (Years)



Figure 5-2. Crossover price line for single product stand with GBM price process

The region to the RHS of the line is the harvesting region and to its LHS is the


continuity region. The form of the crossover line suggests that the optimal strategy will


comprise of harvesting only if the rotation age approaches the terminal age and the


stumpage prices decrease to zero. As the rotation age approaches the terminal age

harvesting at higher stumpage prices becomes feasible. These results conform to the









findings of Thomson (1992a) and the discussion in Plantinga (1998) on the results of a

Geometric Brownian motion price process. The form of the optimal harvesting strategy

implies that harvesting was only feasible to avoid uneconomic outcomes or when there

was a low probability of improving returns by waiting any further in the time left to the

terminal date.

This result for the GBM price process is also confirmed from the results for plain

financial (American) options on non-dividend bearing stocks that are always optimally

held to the maturity/terminal date. The crossover line for stand harvesting is observed

because of the presence of the intermediate expenses.

Sensitivity Analysis

Sensitivity of the results to changes in values of various parameters was considered

next. The land rent was re-estimated to reflect the change in value of the parameter under

consideration. First, the response of the results to changes in intermediate expenses was

considered. The option value corresponding to an increase in intermediate expenses by

$10/annum/acre was $859/acre, a decrease of more than $100/acre. On the other hand the

option value for an increase in intermediate expenses by $40/annum/acre decreased the

option value to $617/acre a drop of about $3 50/acre.

If the higher intermediate expenses are considered to arise from payments for

purchase of insurance against non-marketed undesirable risks, it is possible to see the

effect that catastrophic risks have on the harvesting strategy and option values. Thus, the

observed empirical rotations of less than 30 years could be partly explained by the

presence of non-marketed undesirable risks.

On the other hand, lowering intermediate expenses by $10/annum/acre increased

the option value to $1,088/acre, an increase of more than $100/acre. Thus, positive and









































20 2122 23 2425 2627 2829 30 3132 33 3435 3637 3839 4041 42
Rotation age (Years)


previsible cash flows in the form of, say, non-timber incomes or aesthetic values would

lead to longer optimal rotations.


400-


350

300


S250


S200

E 150

100

50


0


-oUnchanged Intermediate Expenses
-m- Less $5
-a Less $10
-x- Less $20


Figure 5-3. Crossover price lines for different levels of intermediate expenses

Figure 5-3 illustrates the effect of changing the magnitude of intermediate cash

flows. As the intermediate cash flows in the form of expenses or negative cash flows


increase the crossover line shifts to the left towards lower rotation ages


From the option pricing theory it is known that option value is directly related to


the magnitude of the variance of the underlying stochastic asset value. The variance for


stumpage prices estimated from the TMS data may be higher than the variance


experienced by individual pulpwood stand owners for reasons discussed earlier. Higher

variances mean the possibility of higher positive payoffs even while the effect of the


higher negative values is limited to zero.











The results of the sensitivity analysis for different levels of variances confirmed the

known behavior of option values. The option value for a standard deviation value of 0.20

was $765/acre as compared to $966/acre for the base standard deviation of 0.29. The

option value dropped further to $658/acre for a standard deviation of 0. 10.

Figure 5-4 shows that when the variance level is lower the crossover lines lie to the

left of higher variance models so that optimal harvesting at lower rotation ages as well as

lower stumpage prices becomes feasible. This implies that in a situation of large expected

variances arising, say, from an unpredictable regulatory environment, harvesting should


be optimally postponed.


200-

180

160

1 40

S120- Unchanged Standard Deviation 0.29
100 -A- Standard Deviation 0.2











Rotation age (Years)



Figure 5-4. Crossover price line for different levels of standard deviation

Instead of using a convenience yield value of zero, the use of a positive constant

convenience yield will alter the risk-neutral expected drift of the process (Equation 3 -3 8).

For a constant convenience yield of 0.005 the option value dropped to $874/acre,

dropping further to $803/acre for convenience yield value 0.01 and to $755/acre for











convenience yield value 0.015. Since higher levels of convenience yield are associated

with low levels of inventory and associated higher market prices, it suggests that optimal


rotations should be shorter when the markets are tight.


Figure 5-5 plots the effect of different levels of constant convenience yield on the

crossover price lines. It shows the leftward shift of the crossover lines in response to


higher levels of constant positive convenience yields.


200-

180

160

1 40

~ 120 ~Convenience Yield =0
8 'Convenience Yield = 0.005
S100
-A- Convenience Yield = 0.01
80t Convenience Yield = 0.015









20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
Rotation age (Years)



Figure 5-5. Crossover price lines for varying levels of positive constant convenience
yield

It iz also of interest to know if the optimal decision changes for a different current


price i.e., does a higher or lower current stumpage price induce earlier harvesting. The


per acre option value corresponding to a present stumpage price of $1/ton ($76/acre) was

$7/ton ($548/acre). On the other hand the per acre option value for a present stumpage


price of $20/ton ($1,529/acre) was $25/ton ($1,933/acre). The possibility of higher










payoffs as a result of higher current prices inflates the land rent reducing the relative

option values.

Figure 5-6 plots the results of considering different levels of current stumpage

prices. The plots show that the cross-over price lines shift to the left for a price increase

and vice versa. This results partly from the effect of a direct relation between land rent

and current prices. All other things being constant, a higher current timber price increases

the present land value which increases the cost of waiting through the land rent. At the

same time higher present stumpage price also means lower possibility of unfavorable

outcomes but this effect is overwhelmed by the increase in land values.


200-

180

160

140

12 -o- ~Unchanged Stumpage Price
= $7.42/Ton
100 -x-Stumpage Price = $1.00/Ton

-Stumpage Price = $20/Ton
0.. 8 0

S60

40

20


0L L L`rb~~~0 C~O~'b~9 ~ r

Rotation Age (Years)


Figure 5-6.


Crossover price lines for different levels of current stumpage price









Next, by changing the present rotation age of the stand from 20 to 25 and 30 we

can observe the drop in option values commonly associated with financial options as the

time remaining till the terminal date is reduced. This is due to the lower probability of

higher payoffs in the remaining time. For the timber stand, for a present rotation age of

25 the associated option value was $9.97/ton ($999/acre). Similarly, for a present rotation

age of 30 the option value was $8.88/ton ($1,027/acre).

Finally, the effect of a change in the initial planting density was studied. The

current plantation establishment expenses were adjusted to reflect the cost of planting less

plants which effects the estimated land rent, though only marginally. The important

observation is that lower initial planting densities did not change option values which

were $12.62/ton ($909/acre) for 700 tpa and $12.64/ton ($781/acre) for 500 tpa. The

option value calculated earlier for a 800 tpa initial planting density was $12.64/ton.

Comparison with the Dynamic Programming Approach

This section applies the DP approach to the single product slash pine pulpwood

stand with timber prices following a GBM process. The binomial lattice was set up using

Equations 3-28 and 3-29 with the subjective probability given by Equation 3-27 instead

of the risk-neutral probability given by Equation 3-37. The estimated value of the drift pu


for pulpwood stumpage prices was 0.05 (with a standard error of =0.053 or >100%).

A variety of discount rates have been used in published forestry literature using the DP

approach, the most common being a real rate of 5%. Since this analysis was conducted in

nominal terms and the average inflation estimated from the PPI series was 3%, a nominal

discount rate of 8% was used in this DP analysis. Further, some of the published

literature assumes the intermediate costs are constant in real terms. Therefore, for










comparability, future values of intermediate expenses including the estimated land rent

and harvesting and transportation cost were inflated at the average inflation rate of 3%

computed from the PPI series. The land rent was estimated using the DP procedure. All

other parameter values used were unchanged from Table 5-1.

The option value derived from the DP approach, parameterized as above, was

$2,393/acre. This value was more than twice the option value derived using the CC

approach i.e., $966/acre. The use of a discount rate of approximately 12.5% brought

down the estimated option value using the DP approach close to the option value

estimated using the CC approach. As noted and illustrated in Hull (2003) the appropriate

discount rate for options is much higher than the discount rate applicable to the

underlying asset.

First, it should be noted that at 12.5% the discount rate is much higher than typical

rates considered in forestry literature on options analysis. Second, this discount rate is not

a constant but would vary according to the parameter values of the problem. This is

evident from the sensitivity of the option values to parameters exhibited above.

This illustration serves to highlight the problems associated with using the DP

approach in the absence of a method for determining the appropriate discount rate.

A Single Product Stand and the Mean Reverting Price Process

In this section the optimal harvesting strategy for the single product pulpwoodd)

stand is analyzed with a mean reverting FOB price process. The stand description and

management history were identical to those considered for the GBM price process

analysis. The parameters used in the basic analysis are listed in Table5-2.










The problem was modeled by considering only the FOB price for pulpwood as

stochastic following a MR process of the form given by Equation 4-22 with a constant

standard deviation of 0.22 and constant reversion coefficient with value 0. 18.

Table 5-2. Parameter values used in analysis of harvest decision for single product stand
with MR price process


Parameter Effective Date/ Unit Value
Period


FOB price II Qtr 2005 $/ton 21.96
Stumpage price II Qtr 2005 $/ton 7.42
Harvesting and transportation cost II Qtr 2005 $/ton 14.54
Mean FOB price level II Qtr 2005 $/ton 24.44

Initial plantation expenses II Qtr 2005 $/acre 330.00
Estimated land rent II Qtr 2005 $/acre/year 24.00
Other annual expenses II Qtr 2005 $/acre/year 10.00

Estimated standard deviation 07-01-2005 Annual 0.22
of MR price process
Estimated constant reversion 07-01-2005 Annual 0.18
coefficient
Risk free rate 07-01-2005 %//annum 3.64
Constant convenience yield Annual 0.00
Present age of stand 07-01-2005 Years 20.00
Estimated average inflation rate %//annum 3.00


The option value at $1,290 was higher than present stumpage value of $567.

From the form of the crossover price line (drawn after smoothing) for the mean

reverting FOB prices (Figure 5-7) it is evident that the strategy for the optimal harvest is

significantly different than that for the GBM prices. The form of the crossover line

suggests that the optimal strategy would be to harvest if a sufficiently high stumpage was

received at each rotation age, the crossover price declining with the rotation age. These

results are consistent with those reported for the reservation prices obtained using search

algorithms and for other studies with first order autoregressive or mean reverting prices.




Full Text

PAGE 1

CONTINGENT CLAIMS ANALYSIS OF OPTIMAL INVESTMENT DECISION MAKING IN THE MANAGEMENT OF TIMBER STANDS By SHIV NATH MEHROTRA A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2006

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Copyright 2006 by Shiv Nath Mehrotra

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iii ACKNOWLEDGMENTS I am grateful to my supervisory committee chair, Dr. Douglas R. Carter, co-chair, Dr. Janaki R. Alavalapati, and Drs. Donald L. Rockwood, Alan J. Long and Charles B. Moss for their academic guidance and support. I particularly wish to thank Dr. Charles Moss for always finding time to help with th e finance theory as well as for aiding my research in many ways. I thank my family for their support and encouragement.

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iv TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iii LIST OF TABLES.............................................................................................................vi LIST OF FIGURES..........................................................................................................vii ABSTRACT.....................................................................................................................vi ii CHAPTER 1. INTRODUCTION........................................................................................................1 Economic Conditions in Timber Markets.....................................................................1 The Forest Industry in Florida......................................................................................2 Outline of the Investment Problem...............................................................................3 Research Objectives......................................................................................................8 2. PROBLEM BACKGROUND......................................................................................9 Introduction to Slash Pine.............................................................................................9 Slash Pine as a Commercial Plantation Crop........................................................9 Slash Pine Stand Density.....................................................................................11 Thinning of Slash Pine Stands.............................................................................12 Financial Background.................................................................................................13 The Nature of the Harvesting Decision Problem................................................14 Arbitrage Free Pricing.........................................................................................17 Review of Literature on Uncertainty and Timber Stand Management.......................20 3. THE CONTINGENT CLAIMS MODEL AND ESTIMATION METHODOLOGY.....................................................................................................26 The One-Period Model...............................................................................................26 The Deterministic Case.......................................................................................26 The Stochastic Case.............................................................................................29 Form of the Solution for the Stochastic Value Problem......................................31 The Contingent Claims Model....................................................................................31 The Lattice Estimation Models...................................................................................38 The Binomial Lattice Model...............................................................................38

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v The Trinomial Lattice Model fo r a Mean Reverting Process..............................42 The Multinomial Lattice Model for Tw o Underlying Correlated Stochastic Assets...............................................................................................................43 4. APPLICATION OF THE CONTINGENT CLAIMS MODEL.................................45 Who is the Pulpwood Farmer?...................................................................................45 The Return to Land in Timber Stand Investments......................................................50 On the Convenience Yield and the Timber Stand Investment...................................57 Dynamics of the Price Process...................................................................................60 Modeling the Price Process.................................................................................63 The Geometric Brownian Motion Process..........................................................65 Statistical Tests of the Geometric Brownian Motion Model...............................67 The Mean Reverting Process...............................................................................70 Statistical Tests of the Mean Reverting Process Model......................................74 Instantaneous Correlation....................................................................................75 The Data......................................................................................................................7 6 Growth and Yield Equations...............................................................................76 Plantation Establishment Expenses.....................................................................78 Risk-Free Rate of Return.....................................................................................79 The Model Summarized.............................................................................................79 5. RESULTS AND DISCUSSION.................................................................................81 A Single Product Stand and the Geometri c Brownian Motion Price Process............81 Sensitivity Analysis.............................................................................................84 Comparison with the Dyna mic Programming Approach....................................89 A Single Product Stand and the M ean Reverting Price Process.................................90 The Multiple Product Stand and Geometri c Brownian Motion Price Processes........93 Thinning the Single Product Stand and th e Geometric Brownian Motion Price Process....................................................................................................................96 Discussion...................................................................................................................98 Recommendations for Further Research..................................................................104 APPENDIX CORRELATION OF FIRST DIFFERENCES OF AVERAGES OF TWO RANDOM CHAINS......................................................................105 LIST OF REFERENCES.................................................................................................107 BIOGRAPHICAL SKETCH...........................................................................................115

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vi LIST OF TABLES Table page 1-1. Comparison of applied Dynamic Programming and Contingent Claims approaches..................................................................................................................6 2-1. Area of timberland classified as a sl ash pine forest type by ownership class, 1980 and 2000 (Thousand Acres ) ............................................................................10 3-1. Parameter values for a three dimensional lattice.......................................................44 4-1. Florida statewide nomi nal pine stumpage average product price difference and average relative prices (1980-2005).........................................................................46 4-2. The effect of timber product price di fferentiation on optimal Faustmann rotation...47 4-3. The effect of timber product relative prices on optimal Faustmann rotation............47 4-4. Estimated GBM process parameter values for Florida statewide nominal quarterly average pulpwood prices..........................................................................66 4-5. Results of Jarque-Bera test applied to GBM model for Florida statewide nominal quarterly average pulpwood stumpage prices..........................................................70 4-6. Inflation adjusted regression and MR model parameter estimates............................73 4-7. Results of Jarque-Bera test applied to MR model residuals for Florida statewide nominal quarterly average pulpwood stumpage prices............................................75 4-8. Average per acre planta tion establishment expenses for with a 800 seedlings/acre planting density........................................................................................................78 5-1. Parameter values used in analysis of harvest decision for single product stand with GBM price process...........................................................................................82 5-2. Parameter values used in analysis of harvest decision for single product stand with MR price process..............................................................................................91 5-3. Parameter values used in analysis of harvest decision for multiproduct stand with GBM price processes...............................................................................................93

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vii LIST OF FIGURES Figure page 1-1. Florida statewide nominal quarterly average pine stumpage prices (1976-2005 II qtr)........................................................................................................................... ...1 3-1. Typical evolution of even-aged stand and stumpage values for the Faustmann analysis.....................................................................................................................27 4-1. Sample autocorrelation function plot for nominal Florid a statewide pulpwood stumpage instantaneous ra te of price changes..........................................................69 4-2. Sample autocorrelation function plot for nominal Florid a statewide pulpwood stumpage price MR model regression residuals.......................................................75 5-1. Total per acre merchantable yield curve for slash pine stand....................................81 5-2. Crossover price line for single product stand with GBM price process....................83 5-3. Crossover price lines for different levels of intermediate expenses..........................85 5-4. Crossover price line for differe nt levels of standard deviation.................................86 5-5. Crossover price lines for varying levels of positive constant convenience yield......87 5-6. Crossover price lines for different levels of current stumpage price.........................88 5-7. Crossover price line for single product stand with MR price process.......................92 5-8. Merchantable yield curves for pulpwood and CNS...................................................93 5-9. Crossover price lines for multiproduct stand.............................................................95 5-10. Single product stand merchantable yield cu rves with single thinning at different ages........................................................................................................................... 97

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viii Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy CONTINGENT CLAIMS ANALYSIS OF OPTIMAL INVESTMENT DECISION MAKING IN THE MANAGEMENT OF TIMBER STANDS By Shiv Nath Mehrotra August 2006 Chair: Douglas R. Carter Cochair: Janaki R. Alavalapati Major Department: Forest Resources and Conservation The treatment of timber stand investment problems involving stochastic market prices for timber and multiple options can be considerably improved by the application of real options analysis. The an alysis is applied to the d ilemma of mature slash pine pulpwood crop holders in Florida facing depr essed markets for their product. Using a contingent claims approach an arbitrage fr ee market enforced value is put on the option of waiting with or without commercial thi nning, which when compared with the present market value of stumpage allows an optimal decision to be taken. Results for two competing models of tim ber price process su pport the decision to wait for a representative unthinned 20-year-old cutover slash pine pulpwood stand with site index 60 (age 25) and in itial planting density 800 trees per acre. The present (III Qtr 2005) value of stumpage is $567/acre as compar ed to the calculated option value for the Geometric Brownian motion price proce ss of $966/acre and $1,290/ acre for the Mean

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ix Reverting price process. When the analysis differentiates the merchantable timber yield between products pulpwood and chip-n-saw wi th correlated Geometric Brownian motion price processes the option value rises to $1,325/acre for a stumpage market value of $585/acre. On the other hand the commercial thinning option holds no value to the single product stand investment when the poor response of the slash pine spec ies to late rotation thinning is accounted for. The analysis shows that the measurement of option values embedded in the timber stand asset is hampered by the lack of availa bility of market information. The absence of a market for the significant catastrophic risk associated with the asset as well other nonmarketed risks also hampers the measurement of option values. The analysis highlights the importance of access to market information for optimal investment decision making for timber stand management. It concludes that stand owners can realize the full value of the signifi cant managerial flexibility in their stands only when access to market information im proves and markets for trading in risks develop for the timber stand investment.

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1 CHAPTER 1 INTRODUCTION Economic Conditions in Timber Markets Pine pulpwood prices in Flor ida have been declining si nce the peaks of the early 1990Â’s (Figure 1-1). After reachi ng levels last seen in the early 1980Â’s, in 2005 the prices have shown signs of a weak recovery. The trend in pulpwood markets reflects the impact of downturn in pulp and paper manufacturing resulting from several factors (Ince 2002) like: 0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.001976 1978 1979 1980 1981 1983 1984 1985 1986 1988 1989 1990 1991 1993 1994 1995 1996 1998 1999 2000 2001 2003 2004Year Stumpage Price O.B. ($/Ton) Saw Timber Price Chip-n-saw Price Pulpwood Prices Source: Timber Mart-South Figure 1-1. Florida statewide nominal quarterly average pi ne stumpage prices (1976-2005 II qtr) 1. A strong US dollar, rising imports and weakness in export markets since 1997. 2. Mill ownership consolidation and closures.

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2 3. Increased paper recyclin g along with continued e xpansion in pulpwood supply from managed pine plantations, particularly in the US South. In a discussion of the findings and projecti ons of the Resource Planning Act (RPA), 2000 Timber Assessment (Haynes 2003), Ince (2 002) has noted that the pulp and paper industry sector has witnessed a fall in capacity growth since 1998 with capacity actually declining in 2001. The report pr ojects that US wide pulpwood stumpage prices would stabilize in the near term w ith a gradual recovery, but woul d not increase appreciably for several decades into the futu re. With anticipated expansi on in southern pine pulpwood supply from maturing plantations, pine stumpage prices are projected to further subside after 2015. Pine pulpwood stumpage prices are not projected to return to the peak levels of the early 1990Â’s in the foreseeable future (Adams 2002). Nevertheless, the US South is projected to remain the dominant region in production of fiber products and pulpwood demand and supply. The Forest Industry in Florida Florida has over 16 million acres of forests, representing 47% of the stateÂ’s land area. Non-industrial private forest (NIPF) owners hold approximat ely 53% of the over 14 million acres of timberland in the state (Carter and Jokela 2002). The forest based industry in Florida has a larg e presence with close to 700 manufacturing facilities. The industry produces over 900,000 tons of pa per and over 1,700,000 tons of paperboard annually apart from hardwood and softwood lumber and structur al panels (AF&PA 2003). Pulpwood and sawlogs are the principa l roundwood products in Florida accounting for up to 80% of the output by volume. Pulpwo od alone accounted for more than 50% of the roundwood output in 1999. NIPF land contri buted 45% of the to tal roundwood output

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3 while an equal percent came from industry held timberlands. Slash and longleaf pine provided 78% of the softwood roundw ood output (Bentley et al. 2002). Forest lands produce many benefits for th eir owners who express diverse reasons for owning them. A survey of private forest land owners in the US South by Birch (1997) found that nearly 38% of the private forestland owners hold forestland primarily because it is simply a part of the farm or residen ce. Recreation and esthetic enjoyment was the primary motive for 17% while 9% of the owners stated farm or domestic use as the most important reason for owning forest land. Among st commercial motives, land investment was the primary motive for 12% of the owners At the same time expected increase in land value in the following 10 years was listed as the most important benefit from owning timberland by 27% of landowners accoun ting for 21% of private forests listed. Significantly, timber production was the prim ary motive for only 4% of the private forestland owners, but these owners control 35% of the private fo restland. Similarly, only 7% of the owners have listed income from the sale of timber as the most important benefit in the following 10 y ears, but they control 40% of the private forest. Outline of the Investment Problem Timberland is defined as land that either bears or has the potential to bear merchantable quality timber in economic quantities. The US has nearly 740 million acres of forestland, of which 480 million acres is cla ssified as timberland and the rest are either preserves or lands too poor to produce ade quate quality or quantity of merchantable timber (Wilson 2000). Small private woodlot ownership (<100 acr es) accounts for more than 90% of NIPF timberland holdings in the US and remain s a significant part of the investment pattern (Birch 1996). The prolonged depressi on in pulpwood prices poses a dilemma for

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4 NIPF small woodlot timber cultivators in Florida who are holding a mature pulpwood crop. These pulpwood farmers must decide abou t harvesting or extending the rotation. The option to extend the rotation and wait out the depressed markets brings further options like partial realiza tion of revenues immediately through commercial thinnings. These decisions must be made in the face of uncertainty over the futu re market price(s) for their timber product(s). Slash pine pulpwood stand owners must also contend with the fact that the species does not respond well to late rotati on thinnings, limiti ng the options for investing in late rota tion products (Johnson 1961). The timber stand investment is subject to several risks, mark eted as well as nonmarketed (e.g., risk of damage to the physic al assets in the absence of insurance). Understanding and incorporating these risks in to management decisions is crucial to increasing the efficiency of the investment The asset value/price risk is the most common form of risk encountered by all i nvestors. For most forms of investments markets have developed several financial in struments for trading in risk. Insurance products are the most common while others su ch as forwards, futures and options are now widely used. Unfortunately, timberland investments lag behind in this respect. Institutional timberland investors, with their la rger resources, deal with specific risk by diversification (geographic, product). Small woodlot owne rs must contend with the greatest exposure to risk. Investment risk in timber markets has b een long recognized and extensively treated in literature. As a result, on the one hand, ther e is a better apprecia tion of the nature and importance of correctly modeling the stochastic variables, and on th e other hand, there is improved insight into the nature of the inve stment problem faced by the decision maker.

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5 Despite the considerable progress, no single universally acceptable approach or model has yet been developed for analyzing and so lving these problems. Due to the financial nature of the problem, developments in financial literature have mostly preceded progress in forest economics research. In the last decades, the most important and influential development in financial theory has been that of the option pricing theory. Several timber investment problems are in the nature of contingent claims and best treated by the application of option pricing theo ry or what is described as real options analysis (since the investments are real as opposed to financial instruments). It is known that for investment d ecisions characterized by uncertainty, irreversibility, and the ability to postpone, investors set a higher hurdle rate. Stand management decisions like commercial thinning and final harvest share these characteristics. Options analysis provides a means for valuing the flexibility in these investments. There are two approaches to options analysis, namely, the dynamic programming (DP) approach and the conti ngent claims (CC) approach. Almost all treatment of investment problems in forestry literature uses the DP approach to options analysis. Despite its popularity in research, the applied DP approach has some drawbacks which limit its utility for rese arch or empirical applicati ons. The CC valuation is free from these limitations. Some important features of the application of the two approaches are compared in Table 1.1. The most critical problem is that application of the DP approach requires the determination of an appropriate discount rate. In the absen ce of theoretical guidance on the subject studies are forced to use arbitrary discount rates with little relation to the risk of the asset. For example, Insley (2002) us es a discount rate of 5%, Insley and Rollins

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6 (2005) use 3% and 5% real di scount rates alternately, while Plantinga (1998) uses a 5% “real risk-free” discount rate even though the analysis uses subjective probabilities. No justification is offered for the choice of the discount rate (P lantinga (1998) cites Morck et al. (1989) for providing a rate “typical” to timber investment ). Hull (2003) illustrates the difference between the discount rate applic able to the underlying instrument and the option on it. For a 16% discount rate applic able to the underlying, the illustration shows that the discount rate on the option is 42.6%. Explaining the hi gher discount rate required for the option, Hull (2003) mentions that a position on the option is riskier than the position on the underlying. Another problem with the use of arbitrar y discount rates is that the results of different studies are not comparable. Table 1-1. Comparison of applied Dynami c Programming and Contingent Claims approaches _____________________________________________________________________________ Dynamic Programming Approach Contingent Claims Approach _____________________________________________________________________________ 1. Requires the use of an externally Uses a risk-free discount rate that determined discount rate. This is reliably estimated from existing discount rate is unobservable market instruments. (unless the option itself is traded). The discount rates used in published forestry literature bear no relation to the risk of the asset. 2. Published forestry literature does Distinguishes between marketed and not specify whether the marketed, non-marketed components of the the non-marketed or both components assets risk. Applies only to marketed of the asset’s risk are being treated. risk. Extensions have been proposed to account for non-marketed risk 3. Risk preferences are treated It is a risk neutral analysis. inconsistently in published forestry literature. 4. Requires use of historical estimates Replaces the drift with the risk-free of mean return or drift which is rate of return. Estimates of susceptible to large statistical erro rs. historical variance are relatively stable. ________________________________________________________________________

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7 Similarly, none of the published research on options analysis in forestry specifies whether the marketed, the non-marketed or bo th risks are being tr eated. Since the only stochasticity allowed is in the timber price, it may be possible to infer that the marketed risk is the object of the analysis. But such inference would challeng e the validity of some of their conclusions. For example, Plantinga (1998) concludes th at reservation price policies, on an average, increase rotation lengths in comparison to the Faustmann rotation, while management costs decrease rota tion lengths. By includ ing a notional cost of hedging against non-marketed risks (insur ance purchase) in the analysis as a management cost any conclusion regarding th e rotation extension e ffect of reservation prices policies would be cast in doubt without better market data on the size of these hedging costs. Failure to highlight the treatment of risk preferences in the analysis is another source of confusion. Some studies like Brazee and Mendelsohn (1988) specify that the decision maker is risk neutral. Knowing this helps individuals to interpret the results according to their risk preferences. But when risk preferences are not specified, as in Insley (2002) for example, and there is c onfusion over the discount rate applied, the results produced by the analysis lose interpretative value. Real options analysis as it is applied through contingent claims valuation is itself a nascent branch of the option pricing theory which has developed principally by extending option pricing concepts to the valuation of real assets. Ther e is increasing recognition of the shortcomings of the techniques develope d for pricing financial asset options when applied to real assets and several modified approaches have been proposed. Nevertheless, application of real options an alysis to timber investment decisions offers an opportunity

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8 to take advantage of a unified financial theory to treat the su bject and thus obtain a richer interpretation of the results. Research Objectives The general objective of this study is to apply contingent claims analysis to examine typical flexible investment decisi ons in timber stand management, made under uncertainty. The analysis is applied to th e options facing the NIPF small woodlot owner in Florida holding a mature even aged sl ash pine pulpwood crop. Th e specific objectives are 1. To analyze and compare the optimal clear-cut harvesting decision for a single product, i.e., pulpwood, producing stand with Geometric Brownian Motion (GBM) and Mean Reverting (MR) price process alternately. 2. To analyze the optimal clear-cut harvesting decision for a multiple product, i.e. pulpwood and chip-n-saw, producing stand w ith their prices following correlated GBM processes. 3. To analyze the optimal clear-cut harvesting decision with an option for a commercial thinning for a single product, i.e., pulpwood, producing stand with a GBM price process.

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9 CHAPTER 2 PROBLEM BACKGROUND Introduction to Slash Pine Slash pine ( Pinus elliottii var. elliottii ) is one of the hard yellow pines indigenous to the southeastern United States. Other o ccasional names for the specie are southern pine, yellow slash pine, swamp pine, pitch pine, and Cuban pine. Along with the most frequently encountered variety P. elliottii var. elliottii the other recognized variety is P. elliottii var. densa which grows naturally only in the s outhern half of peninsula Florida and in the Keys (Lohrey and Kossuth 1990). The distribution of slash pine within its natural range (8 latitude and 10 longitude) was initially determined by its suscep tibility to fire injury during the seedling stage. Slash pine grew thr oughout the flatwoods of north Fl orida and south Georgia as well as along streams and the edges of swamps and bays. Within these areas either ample soil moisture or standing water protected young seedlings from frequent wildfires in young forests (Lohrey and Kossuth 1990). Slash pine is a frequent and abundant seed producer and is characterized by rapid early growth. After the sapling stage it can withstand wildfires a nd rooting by wild hogs which has helped it to spread to drier sites (Lohrey and Kossuth 1990). Slash Pine as a Commercial Plantation Crop Florida has the largest area of timberland (B arnett and Sheffield 2004) classified as slash pine forest type (49%) while noni ndustrial private landowners hold the largest portion of slash pine tim berland (Table 2-1)

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10 Table 2-1. Area of timberland classified as a slash pine forest type, by ownership class, 1980 and 2000 (Thousand Acres ) _____________________________________________________ Ownership Class 1980 2000 _____________________________________________________ National Forest 522 493 Other Public 569 684 Forest Industry 4,649 3,719 Nonindustrial Private 7,039 5,479 _____________________________________________________ Total 12,779 10,375 _____________________________________________________ Source: Barnett and Sheffield, 2004 Slash pine makes rapid volume growth at early ages and is adaptable to short rotations under intensive mana gement. Almost three-fourths of the 50-year yield is produced by age 30, regardless of stand basal area. Below age 30, maximum cubic volume yields are usually produced in unt hinned plantations, so landowners seeking maximum yields on a short ro tation will seldom find commercial thinning beneficial. Where sawtimber is the objective, commercia l thinnings provide early revenues while improving the growth and quality of the sa wtimber and maintaining the stands in a vigorous and healthy conditi on (Lohrey and Kossuth 1990). A study by Barnett and Sheffi eld (2004) found that a majority (59%) of the slash pine inventory volume in plantations and natu ral stands was in the <10” dbh class while about 25% of the stands were less than 8 years old. The study concluded that this confirmed the notion that slash pine rotations are typically le ss than 30 years and that the stands are intensively managed. Plantation yields are influenced by previ ous land use and interspecies competition. Early yields are usually hi ghest on recently abandoned fields where the young trees apparently benefit from the residual effects of tillage or fertilizer and the nearly complete lack of vegetative competiti on. Plantations established after the harvest of natural stands

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11 and without any site treatment other than burning generally have lower survival and, consequently, lower basal area and volume than stands on old fields. Yields in plantations established after timber harvest and intensive site preparation such as disking or bedding are usually intermediate. Comparing slash pine to loblolly pine ( Pinus taeda L. ), Shiver (2004) notes that slash pine may be preferred over loblolly pi ne for reasons other than wood yields. For instance, slash pine would be the favored sp ecies for landowners who want to sell pine straw. Slash pine also prunes itself much bett er than loblolly, and for solid wood products the lumber grade will probably be higher for slash pine. Slash pine is more resistant to southern pine beetle ( Dendroctonus frontalis Zimmermann) attack than loblolly and it is rarely bothered with pine tip moth ( Rhyacionia frustrana (Comstock)), which can decimate young loblolly stands. Slash Pine Stand Density Dickens and Will (2004) discuss the eff ects of stand density choices on the management of slash pine stands. The c hoice of initial planting density and its management during the rotation depends on landowner objectives like maximizing revenues from pine straw, obtaining intermed iate cash flows from thinnings or growing high value large diameter class timber produc ts. High planting dens ity in slash pine stands decreases tree diameter growth as well as suppresses the tree height growth to a lesser extent, but total volume production pe r unit of land is increased. However, the volume increment observed for early rotation ages soon peaks and converges to that of lower density stands as the growth rate of high density stands reach a maximum earlier. Citing a study at the Plantation Management Research Cooperative, Georgia, Dickens

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12 and Will (2004) remark that management intensity does not change the effects of stand density. Dickens and Will (2004) mention that higher density plantings achieve canopy closure, site utilization, a nd pine straw production earlier than lower density plantings under the same level of management. Higher pl anting densities also may be beneficial on cut-over sites with low site preparation and management inputs. The higher planting densities help crop trees occupy the site, wh ereas the lower planting densities may permit high interspecific competition until much late r during stand development, reducing early stand volume production. Thinning of Slash Pine Stands Mann and Enghardt (1972) describe the resu lts of subjecting slash pine stands to three levels of thinnings at ages 10, 13 & 16. Early thinnings removed the diseased trees while later thinnings concentrat ed on release of better stem s. Their study concluded that early and heavy thinnings increased diamet er growth but reduced volume growth. The longer thinnings were deferred, the slower wa s the response in diameter growth. They concluded that age 10 was too early for a thi nning as most of the timber harvested was not merchantable and volume growth was lost, even though the diameter increment results were the best. The decision between thinning at ages 13 and 16 depended on the end product, the ability to realize merchant able volumes in thinnings and the loss of volume growth. They recommend that short rotation pulpwood crops were best left unthinned as the unthinned stands had good volume growth. Quoting Mann and Enghardt (1972) “volume growth is good, no costs are in curred for marking, there are fewer small trees to harvest and stand dist urbances that may attract bark beetle are avoided” (Mann and Enghardt 1972, p.10).

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13 Johnson (1961) has discussed the results of a study of thinning conducted on heavily stocked industrial slash pine stands of merchantable size. The study found that slash pine does not respond well to late release i.e., if it ha s been grown in moderately dense stands for the first 20 to 25 years of its life. It does not sta gnate, except perhaps on the poorest sites, but it cannot be expected to respond to cultural treatments such as thinnings as promptly or to the degree desired. Johnson (1961) observes that the typical thinning operation that removes four to six cords of wood from well-stocked stands is nothing more than an interim recovery of capital from the forestry enterprise. These thinnings do not stimulate growth of the residual stand or total production The study found no real increase in total volume produc tion or in average size of trees from commercial thinnings in slash pine stands being managed on short rotations for small products. Johnson (1961) concludes that silvicultura l considerations for commercial thinning in small product slash pine forest ma nagement are secondary to commercial considerations because of its re sponse to intermediate cuttings. Financial Background The timber farming investment exposes the investor to the risks that the asset carries. These risks come in the form of mark eted risks like the volatile market price for the timber products or non-marketed risks that also effect the value of the investment such as hazards that threaten the investment in the form of fire, pests, adverse weather etc. Usually, investors separate the spectrum of risks taken on by them from an investment into core and non-core risks. The core risk could be the market price of the investments output or product. Th is is the risk the investor expects to profit out of and

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14 likes to retain. The non-core risk like the non-marketed risks listed above are undesirable and the investor would ideally like to transf er such risks. A common market instrument for risk transfer is the insurance produc t. By paying a price one can transfer the undesirable risk to the market. If the non-ma rketed risks associated with the timber investment were marketed, the market data av ailable can be incorpor ated into investment analysis. In the absence of markets for a part or all of an assets risk, the common asset pricing theories are no t applicable and altern ate methods have to be applied. The analysis in this study is restricted to the marketed risk in the form of timber price risk only. The Nature of the Harvesting Decision Problem Following a price responsive harvesting regime, the slash pine pulpwood farming investor holding a mature crop and facing a stochastically ev olving pulpwood market price would like to know the best time for se lling his crop. From his knowledge of past movements of market price for pulpwood the investor knows that th e present price is lower than the average of prices in the r ecent past. He may sell the crop at the present price but significantly he ha s the option to hold the crop. Th e crop is still growing, both in size and possibly in value, and that pr ovides incentive to hold the harvest. But the market price is volatile. The future market price for pulpwood cannot be predicted with certainty. How does the investor decide his immediate action; sell or hold? While equilibrium asset values are determ ined by their productiv e capacities their instantaneous market values are determined by the ever changing market forces. Asset holders would like to earn a fair compensation on their investment i.e., the principal plus a return for the risk undertaken by holding the investment over time. But there is no guarantee to earning a ‘fair’ retu rn in the market place. Usually investors have a finite

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15 time frame for holding an asset and must reali ze the best value for their asset in this period. The decision to hold the asset for a futu re sale date is a gamble, an act of speculation. It carries the risk of loss as well as the lure of profit. But a ll investments in risky assets are speculative activities. One i nvestment may be more risky than another but one market equilibrium theory in the form of the Capital Asset Pricing Model (CAPM) assures us that their expected returns are pr oportional to their ris k, specifically to the systematic or non-diversifiabl e portion of their risk. The CAPM theory, development of which is simultaneously attributed to Sh arpe (1963, 1964) and Lintner (1965a, 1965b) amongst others, has it that at any point in ti me each marketed asset has an associated equilibrium rate of return which is a function of its covariance with the market portfolio and proportional to the market pr ice of risk. The expression ‘rat e of return’ refers to the capital appreciation plus cash payout, if any, ove r a period of time, expressed as a ratio to the asset value at the co mmencement of the period. If all risky investments are gambles, how does one choose amongst the enormous variety of gambles that are available in the market place? Once again, financial theory informs us that the choice amongst risky assets depends on the risk attitudes of individuals. Individuals w ould apportion their wealth am ongst a portfolio of assets (which serves to eliminate the non-systematic risk of the assets). The portfolio is constructed to match the risk-return tradeo ff sought by the individual. Once chosen, how does one decide how long to hold an asset? Th e risk associated with every asset as well as its expected return ch anges over time. Over a peri od of time the risk-return

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16 characteristic of a particular asset may lose its appeal to the indivi dualÂ’s portfolio which itself keeps changing with maturing of risk attitudes over time. Returning to the pulpwood farmerÂ’s deci sion problem, the question boils down to this: How does the pulpwood farmer decide whether his investment is worth holding anymore? It follows from the arguments a bove that the crop would be worth holding as long it can be expected to earn a return comm ensurate with its risk. But, how is the comparison between the expected rate of return and the required rate of return achieved? The usual financial technique is to subjectively estimate the expected cash flows from the asset, discount them to the present using a risk-adjusted discount rate, and compare the resulting value to the present market value of the asset. If the expected discounted value is higher, then the expected rate of re turn over the future relevant period under consideration is higher than th e required rate of return. A nd how does this work? It works because the required rate of re turn and the risk-adjusted discount rate are different names for the same value. The expected equilibri um rate of return generated by the CAPM represents the average return for all assets shar ing the same risk characteristics or in other words, the opportunity cost. When we use th e risk-adjusted discount rate to calculate the present value of the future cash flows, we ar e in effect accounting for the required rate of return. The discounting apportions the future cash flows between the required rate of return and residual value, if any. Can discounted cash flow (DCF) analysis be used to solve the pulpwood farmerÂ’s harvesting problem? The pulpwood farmerÂ’s valuation problem is compounded by the ability to actively manage the investment (fle xibility) or more specifically, the ability to postpone the harvest decision should the need arise. Not only do decision makers have to

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17 deal with an uncertain future market va lue for the pulpwood crop but they must also factor in the response to the possible values. The termination date or harvest date of the timber stand investment and thus its payoff is not fixed or predetermined. Traditional DCF analysis can deal with the price un certainty by the technique of subjective expectations but has no answer for flexibility of cash flow timings. This shortcoming has been overcome by decision analysis tools like decision trees or simulation to account for the state responsive future cash flows. So, are tools like decision trees or simulation techniques the answer to the pulpwood fa rmerÂ’s dilemma? Almost, except that the appropriate discount rate sti ll needs to be determined. Arbitrage Free Pricing Despite widespread recogniti on of its shortcomings, the CAPM generated expected rate of return is most commonly used as the risk-adjusted discount ra te appropriate to an investment. It turns out that while the mean -variance analysis led school of equilibrium asset pricing does a credible job of explaining expected returns on assets with linear risk they fail to deal with non-linear risk of the type associated with assets whose payoffs are contingent. Hull (2003) provides an illustration to show that the risk (and hence discount rates) of contingent claims is much highe r than that of the underlying asset. The pulpwood farmer holds an asset with a contingent claim because the payoff from his asset over any period is contingent on a favorable price being offered by the market for his crop. There are two alternate though equivalent techniques for valuing a risky asset by discounting its expected future cash flows. One, as already described involves an adjustment to the discount rate to account fo r risk. The other method adjusts the expected cash flows (or equivalently, the probability distribution of futu re cash flows) and uses the

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18 risk-free rate to discount the resulting certainty equivalent of the future cash flows. The CC valuation procedure follows this certain ty equivalent approach. The argument is based on the Law of One Price (LOP). The LOP argues that in a perfect market, in equilibrium, only one price for each asset, irre spective of individual risk preferences, can exist as all competing prices woul d be wiped out by arbitrageurs. Baxter and Rennie (1996) illustrate the difference between expectation pricing and arbitrage pricing using the example of a fo rward trade. Suppose one is asked by a buyer to quote today a unit price for selling a commodity at a future dateT. A fair quote would be one that yields no sure pr ofit to either party or in ot her words provides no arbitrage opportunities. Using expectation pr icing, the seller may believe th at the fair price to quote would be the statistical average or expected price of the commodity, TS where TSis the unit price of the commodity at timeTand Eis the expectation operator. But a statistical average would turn out to be the true price only by coincidence and thus could be the source of significant loss to the seller. The market enforces an arbitrage free price for such trades using a different mechanism. If the borrowing/lending rate isr, then the market enforced price for the forward trade is0 rTSe. This price follows the logic that it is the cost that either party would incur by borrowing funds at the rate r to purchase the commodity today and store it for the necessary duration (assuming no storag e costs). This price would be different from the expected price, yet offer no arbitrage opportunities. The arbitrage free approach to the problem of valuing financial options was first solved by Black and Scholes (1973) using a replicating portfolio technique. The replicating portfolio technique involves finding an asset or combination of assets with

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19 known values, with payoffs that exactly match the payoffs of the contingent claim. Then, using the LOP it can be argued that the continge nt claim must have the same value as the replicating portfolio. Financial options are contingent claims whose payoffs depend on some underlying basic financial asset. These instruments are very popular with hedgers or risk managers. The underlying argument to the equilibriu m asset pricing methods is the no arbitrage condition. The no arbitrage condition requires that the equi librium prices of assets should be consistent in a way that there is no possibility of riskless profit. A complete market offers no arbitrage opportuni ties as there exists a unique probability distribution under which the prices of all marketed assets are proportional to their expected values. This unique distribution is called a risk neutral pr obability distribution of the market. The expected rate of return on every risky asset is equal to the risk-free rate of return when expectations are calcula ted with respect to the market risk neutral distribution. Copeland et al. (2004) define a complete ma rket as one in which for every future state there is a combination of traded assets that is equivalent to a pure state contingent claim. A pure state contingent claim is a securi ty with a payoff of one unit if a particular state occurs, and nothing otherw ise. In other words, when th e number of unique linearly independent securities equals the total number of alternative future states of nature, the market is said to be complete. Equilibrium asset pricing theories have been developed with a set of simplifying assumptions regarding the market. In addition to completeness and pure competition, CC analysis theory assumes that the market is perfect i.e., it is characterized by

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20 1.An absence of transa ction costs & taxes 2.Infinite divisibility of assets. 3.A common borrowing and lending rate. 4.No restrictions on short sales or the use of its proceeds. 5.Continuous trading. 6.Costless access to full information. Review of Literature on Uncertainty and Timber Stand Management The published literature on treatment of uncer tainty in timber stand management is reviewed here from an evolutionary perspec tive. A selected few papers are reviewed as examples of a category of research. The literature dealing with sta tic analysis of financial ma turity of timber stands is vast and diverse. Including the seminal anal ysis of Faustmann (1849) several approaches to the problem have been developed. The early work on static analysis has been summarized by Gaffney (1960) and Bentley a nd Teeguarden (1965). These approaches range from the zero interest rate models to pr esent net worth models and internal rate of return models. The Soil Rent/Land Expecta tion Value (LEV) model, also known as the Faustmann-Ohlin-Pressler model, is now accepted as the correct static financial maturity approach. However, the static models are bui lt on a number of critical assumptions which erode the practical value of the analysis. Fail ure to deal with the random nature of stand values is a prominent shortcoming. Uncertain fu ture values mean that the date of optimal harvest cannot be determined in advance but must be price responsive i.e., it must depend on the movement of prices and stand yield am ongst other things. The harvest decision is local to the time of decision and it is now recognized that a dynamic approach to address the stochastic nature of timb er values is appropriate. Amongst the first to treat stochasticity in stand management, Norstom (1975) uses DP to determine the optimal harvest with a stochastic timber market price. The stochastic

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21 variable was modeled using tr ansition matrices as in Ga ssmann (1988), who dealt with harvesting in the presence of fire risk. The us e of transition matrices has persisted with Teeter et al. (1993) in the determination of the economic strategies for stand density management with stochastic prices. However, much advance followed in mode ling stochasticity with the introduction of the use of diffusion processes in investment theory. Brock et al. (1982) illustrated the optimal stopping problem in stochastic fi nance using the example of a harvesting problem over a single rotation of a tree with a value that grows according to a diffusion process. Miller and Voltair e (1980, 1983) followed up, extending the analysis to the multiple rotation problems. Clarke and Reed (1989) obtained an analytical solution using the Myopic Look Ahead (MLA) approach, allo wing for simultaneous stochasticity in timber price and yield. These papers illu strate the use of stochastic dynamic programming for stylized problems which ar e removed from the pr actical problems in forestry e.g., they ignore the costs in forestry. Modeling the empirical forestry problem, Yin and Newman (1995) modified Clarke and Reed (1989) to incorporate annual administrative and land rental costs as exogenous parameters. However, while acknowledging opti on costs, they chose to ignore them for simplicity. Also, as noted by Gaffney (1960) th e solution to the optimal harvest problem is elusive because the land use has no predetermined cost and the solution calls for simultaneous determination of site rent and financial maturity. Since land in forestry investment is typically owned, not leased or rented, accounting for the unknown market land rental has been one objective of financial maturity analysis since Faustmann (1849).

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22 In the meanwhile, the use of search models to develop a reservation price approach gained popularity with papers by Brazee and Mendelsohn (1988) and others. The technique of the search models is not unlike the DP approach to contingent claims. The approach differs from the CC approach in solution methodology and in the interpretation of the results. Fina et al. ( 2001) presents an extension of the reservation price approach using search models to consider de bt repayment amongst other things. Following the landmark Black and Scholes (1973) paper the development of methodology for the valuation of contingent claims has progresse d rapidly. A useful simplification in the form of the discrete time binomial lattice to approximate the stochastic process was presented by Cox et al. (1979). Other tec hniques for obtaining numerical approximations have been developed including the trinomial approximation, the finite difference methods, Monte Carlo si mulations and numerical integration. Geske and Shastri (1985) provide a review of th e approximation techniques developed for valuation of options. An important simultaneous line of research has been the study of the nature of stochasticity in timber prices. Washburn a nd Binkley (1990a) tested for weak form efficiency in southern pine stumpage ma rkets and reported that annual and quarterly average prices display efficiency, but also poi nt out that monthly averages display serial correlation. Yin and Newman (1996) found evid ence of stationarity in monthly and quarterly southern pine time series price data Since reported prices for timber are in the form of period averages, researchers have to contend with unrav eling the effect of averaging on the statistical properties of the price series. Working (1960) demonstrated the introduction of serial correla tion in averaged price series, not present in the original

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23 series. However, Haight and Holmes (1991) demonstrated that serially correlated averaged price series tends to behave as a random walk. The lack of conclusive data on the presence or absence of sta tionarity in timber price data is because of the imperfections of the data available for analysis. Desp ite the lack of unanimity on the empirical evidence there is some theoretical su pport for the mean reversion (negative autoregression) arising from the knowledge that commodity prices could not exhibit arbitrarily large deviations from long term marginal cost of production without feeling the effects of the forces of demand and supply (Schwartz 1997). The use of contingent claim analysis is a relatively recent development in stand management literature. Morck et al. (1989) use real options analysis to solve for the problem of operating a fixed term lease on a st anding forest with the option to control the cut rate. Zinkhan (1991, 1992) and Thomson ( 1992b) used option analysis to study the optimal switching to alternate land use (agriculture). Thom son (1992a) used the binomial approximation method to price the option value of a timber stand with multiple rotations for a GBM price process. The paper demonstrates a comprehensive treatment of the harvest problem, incorporating the option value of abandonment and switching to an alternate land use. Plantinga (1997) illustrated the valuation of a contingent claim on a timber stand for the mean-reverting and drif tless random walk price processes, using a DP approach attributed to Fisher and Ha nemann (1986). Yoshimoto and Shoji (1998) use the binomial tree approach to model a GBM process for timber prices in Japan and solve for the optimal rotation ages. Insley (2002) advocated the mean-re verting process for price stochasticity. The paper incorporates amenity values and uses harvesting costs as an

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24 exercise price to model the harvesting problem over a single rotation as an American call option. In order to obtain a numerical solu tion, the paper uses a discretization of the linear complementarity formulation with an implicit finite difference method. All these studies use a stochastic DP approach with an arbitrary discount rate. Hughes (2000) used the Black-Scholes call option valuation equa tion to value the forest assets sold by the New Zealand Fo restry Corporation in 1996. The option value estimated by him was closer to the actual sa le value than the alternate discounted cash flow analysis. It is a unique case of a study ap plying real options analysis to value a real forestry transaction. Insley and Rollins (2005) solve for the la nd value of a public forest with mean reverting stochastic timber pr ices and managerial flexibility. They use a DP approach to show that by including managerial flexib ility, the option value of land exceeds the Faustmann value (at mean prices) by a fact or of 6.5 for a 3% discount rate. The land value is solved endogenously for an infinite rotation framework. In a break from analysis devoted to th e problems of a single product timber stand Forboseh et al. (1996) study the optimal cl ear cut harvest problem for a multiproduct (pulpwood and sawtimber) stand w ith joint normally distribut ed correlated timber prices. The study extends the reservation price a pproach of Brazee and Mendelsohn (1988) to multiple products and looks at the effect of various levels of prices and correlation on the expected land value and the pr obability of harvest at differe nt rotation ages. A discrete time DP algorithm is used to obtain the solutions. In a similar study, Gong and Yin (2004) st udy the effect of incorporating multiple autocorrelated timber products into the optim al harvest problem. The paper models the

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25 timber prices (pulpwood and sawtimber) as disc rete first order autoregressive processes. Dynamic programming is used to solve for reservation prices. Teeter and Caulfied (1991) use dyna mic programming to demonstrate the determination of optimal density management with stochastic prices using a first order autoregressive price process modeled using a transition probability matrix. The study uses a fixed rotation age and allows multiple thinnings. Brazee and Bulte (2000) analyze an optimal even-aged stand management strategy with the option to thin (fixed intensity) with stochastic timber prices. Using a random draw mechanism for the price process and a backward recursive DP algorithm for locati ng the reservation pri ces, the study finds the existence of an optimal reservation pri ce policy for the thinning option. Lu and Gong (2003) use an optimal stocking level function to determine the optimal thinning as well as a reservation price function to determine th e optimal harvest strategy for a multiproduct stand with stochastic product prices without autocorrelations.

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26 CHAPTER 3 THE CONTINGENT CLAIMS MOD EL AND ESTIMATION METHODOLOGY The One-Period Model In order to develop the application of opti ons analysis to investment problems it is helpful to first examine the nature of one-p eriod optimization models. One-period models for investment decision making operate by comp aring the value of the investment in the beginning of period with its value at the end of the period. The model is first explained in the context of the deterministic Faustmann pr oblem. This is followed by an extension of the logic to the stochastic problem. The Deterministic Case The problem of finding the optimal financ ial stand rotation age is an optimal stopping problem. In the deterministic Faustmann framework, the optimal rotation age is achieved by holding the stand as long as th e (optimal) investment in the stand is compensated by the market at the required rate of return. The value of the immature stand is the value of all net investments in the st and up to the present in cluding the land rental costs and the cost of capital. This means that the value of all investments in the stand (adjusted for positive intermediate cash flows like revenue from thinnings) up to the present compounded at the required rate of retu rn represents the stand value. This value represents fair compensation to the stand owne r for his investment and fair cost to the purchaser who would incur an identical amount in a deterministic world. Therefore, this value represents the fair market value of th e pre-mature stand. The market value of the merchantable timber in the stand, if any, is less than the stand market value in this period.

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27 The stand owner continues to earn the re quired return on his (optimal) investments only till the rotation age is reached when th e value of the merchantable timber in the stand exactly equals the compounded value of investments. Beyond this rotation age the market will only pay for the value of the merchantable timber in the stand. If the stand is held longer than this rotation age, even if no fresh investments other than land rent are made, the market compensation falls short of the compounded value of investments as the value of merchantable timber grows at a lowe r rate. The optimal ro tation age represents the unique point of financial maturity of the st and. Before this age the stand is financially immature and after this age the stand is fi nancially over mature. A typical evolution of the two values is depicted in Figure 3-1. 0 500 1000 1500 2000 2500 3000 3500 4000 4500Yea r s 3 7 11 15 19 23 27 31 35 39 43 47Rotation age (Years)Stand Value ($/Acre) Value of Merchantable Timber Present Value of net investments Figure 3-1. Typical evolution of even-aged stand and stumpage values for the Faustmann analysis

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28 Equivalently, a more familiar way of frami ng this optimization problem is to let the stand owner compare the value of harvesting the stand in the present period to the net (of cost of waiting) discounted values of harvests at all possible future rotation ages. The cost of waiting includes land rent a nd all other intermediate cash flows. More specifically, the comparison is between the value of a harves t decision today and th e net discounted value of the stand in the next period assuming that similar optimal decisions are taken in the future. In this case the stand value represents the discounted value of a future optimal harvest which exactly equals the earlier de fined stand market value consisting of net investment value. Thus, the proble m is cast as a one-period problem. The one-period deterministic Faustmann op timization problem in discrete time can be summarized mathematically by Equation 3-1. () ()max(),() 1 Ft Fttt (3-1) Here, () F= Stand Value function t = Rotation age = Stand termination value or the market va lue of the merchantable timber in the stand = Rate of cash flow (land re ntal expenses, thinnings etc) = Constant discount rate = A discrete interval of time For the period that the decision to hold the stand dominates, the second expression in the bracket is relevant and we ha ve for the holding period Equation 3-2. () ()() 1 Ftt Fttt t (3-2) It may be noted that the only decision requi red of the decision maker is whether to hold the stand or to harvest it. In the standard deterministic case, any intervention

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29 requiring new investments like thinnings is assumed optimally predetermined and the resulting cash flows are only a function of rotation age. This holding expression can be simplified to yield Equation 3-3 for the continuous time 1 ()()() FttdFt dt (3-3) Here, the limit of 0 has been taken. Equation 3-3 clearly expresses the holding condition in perfect competition as one in which the yield (Right Hand Side (RHS)) in the form of the dividend and the capital appr eciation/depreciation or change in market value over the next infinitesimal period equals the required rate of return on the current market value of the asset (Left Hand Side (LHS)). The optimal stopping conditions are ()() FTT (3-4) ()()ttFTT (3-5) In Equation 3-5 the subscript t denotes the derivative of the respective function with respect to the time variable. The first c ondition is simply that at the optimal rotation age T the market value should equal the terminat ion value and the second condition is the tangency or the smooth pasting condition (D ixit and Pindyck 1994) requiring that the slopes of the two functions should be equal. The Stochastic Case In the stochastic value framework, the problem of optimal rotation is equivalent to holding the asset as long as it is expected to earn the required return. With stochastic parameter values, not only are future asset va lues dependent on the realizations of the parameters but the ability to actively ma nage the asset by responding to revealed parameter values induces an option value. Dixit and Pindyck (1994) derive the holding

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30 condition for the stochastic framework using the Bellman equation, which expresses the value as 1'(,)max(,,)(1)(,)|,uFxtxutFxtxu (3-6) Here, () F = Stand value function x = The (vector of) stochastic variable(s). For this analysis it represents the timber price(s) t = Rotation age u = The control or decision variable (opti on to invest) = The rate of cash flow = The discount rate E = Expectation operator = A discrete interval of time This relation means that the present value(,) Fxt from holding the asset is formed as a result of the optimal decisionu taken at the present, which determines the cash flow in the next period and the expected discounted valu e resulting from taking optimal decisions thereon. Distinct from the deterministic case, in this case, the value (and possibly cash flows) depends on the stochastic timber price. Also, the decision can be expanded to include the decisi ons to make new investments in the stand (like thinning) which effects the immediate cash flows as well as expectations of future market values. Similar to the deterministic case, the holding condition can be re-expressed as 1 (,)max(,,)(,)uFxtxutdFxt dt (3-7) To quote Dixit and Pindyck (1994): The equality becomes a no arbitrage or equilibrium condition, expressing the investorÂ’s willingness to hold the asse t. The maximization with respect toumeans the current operation of the asset is be ing managed optimally, bearing in mind not only the immediate payout but also the consequences for future values. (Dixit and Pindyck 1994, p.105)

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31 Form of the Solution for th e Stochastic Value Problem In general the solution to the problem ha s the form of ranges of values of the stochastic variable(s) x Continuation is optimal for a ra nge(s) of values and termination for other(s). But as elaborated by Dix it and Pindyck (1994), economic problems in general have a structured soluti on where there is a single cutoff x with termination optimal on one side and continuation on the ot her. The threshold its elf is a continuous function of time, referred to as the crossover line The continuation optimal side is referred to as the continuation region and the termination optimal side as the termination region As pointed out by Plantinga (1998) the values of the crossover stumpage price line for timber harvesting problems are equivalent to the concept of reservation prices popular in forestry literature. Consequently, the optimal stopping conditi ons for the stochastic case for all t are (Dixit and Pindyck 1994) **((),)((),) Fxttxtt (3-8) **((),)((),)xxFxttxtt (3-9) In Equation 3-9 the subscript x denotes the derivative of the respective function with respect to the variable x The Contingent Claims Model In this section the general theory of CC valuation is developed in the context of the harvest problem. The CC valuation approach is also built on a one-period optimization approach and the discussion of the last sec tion should help to put the following discussion into perspective.

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32 The simplest harvest problem facing the decision maker is as follows: Should the stand be harvested immediately, accepting th e present market value of the timber or should the harvest decision be postponed in e xpectation of a better outcome? That is, the possibility for all optimal in terventions other than harv est is ignored. In a dynamic programming formulation of the problem, us ing the Bellman equation, the problem can be expressed mathematically as follows (Dixit and Pindyck 1994) 1 (,)max(,),(,)(,1)| 1 FxtxtxtFxtx (3-10) Here (,) Fxt is the expected net present value of all current and future cash flows associated with the investment at time t when the decision maker makes all decisions optimally from this point onwards. The stocha stic state variable, timber price in the present problem, is represented by x The immediate cash flow from a decision to hold the investment is denoted by(,) x t The result of optimal deci sions taken in the next period and thereafter will yield value (,1) Fxt which is a random variable today. The expected value of (,1) Fxtis discounted to the pres ent at the discount rate Finally, (,) x t represents the present value of termin ation or the value realized when the investment is fully disposed off today. While we know the present termination valu e, we are interested in learning the value of waiting or the conti nuation value. If the decision to wait is optimally taken then the continuation value is given by 1 (,)(,)(,1)| 1 FxtxtFxtx (3-11) If the increments of time are represented by and 0 the continuation value expressed in continuous time after algebraic manipulation will be

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33 1 (,)(,) FxtxtdF dt (3-12) If it is assumed that the state variable x (timber price) follows a general diffusion process of the form (,)(,) dxxtdtxtdz (3-13) then, using ItoÂ’s Lemma, after algebraic ma nipulation and simplification we obtain the partial differential equation (PDE) 21 0 2xxxtFFFF (3-14) Here,(,) x t (,) x t and (,) x t In typical economic problems the continuation equation will hold for the value of the asset for all x x where x is a critical value of the state variable x with the property that continuation is optimal when the state variable value is on one side of it and stopping or termination is optimal when the state variable value is on its other side This yields the boundary conditions for all t given by Equations 3-8 and 3-9, which the valu e of the asset must m eet at the critical value of the state variable The DP formulation of the problem assu mes that the appropriate discount rate is known or can be determined by some means. An equivalent formulation of the problem can be found using CC valuation. In this fo rm the PDE for the continuation region value is given by 21 ()0 2xxxtFrFFrF (3-15) Here r represents the risk-fr ee rate of return and represents the rate of return shortfall which could be a dividend and/or convenience yi eld. Dixit and Pindyck (1994)

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34 illustrate the derivation of the contingent claim PDE by using the replicating portfolio method. In an alternate general derivation the procedure is to first show that under certain assumption all traded derivative assets must satisfy the no-arbitrage equilibrium relationii ir Here is the expected return on the derivative security, i represents the component of its volatility attributable to an un derlying stochastic variable i and i represents the market price of risk fo r the underlying stochastic variable. Where there is only one underlyi ng stochastic variable the relation simplifies tor Constantinides (1978) derived the cond ition for changing the asset valuation problem in the presence of market risk to one where the market price of risk was zero. The derivation, presented below, proceeds fr om MertonÂ’s (1973) proof of equilibrium security returns satisfying the CAPM relationship pm p mr (3-16) where ()m mr is the market price of risk, the subscript p refers to the project (asset, option etc) and subscript mrefers to the market portfolio which forms the single underlying stochastic variable. Merton (1973) assumed that 1. The markets are perfect with no transaction costs, no ta xes, infinitely divisible securities and continuous trad ing of securities. Investor s can borrow and lend at the same interest rate and short sale of secu rities with full use of proceeds is allowed. 2. The prices of securities are lognormally di stributed. For each security, the expected rate of return per unit time i and variance of return over unit time 2i exist and are finite with 20i The opportunity set is non-st ochastic in the sense that i ,2i and the covariance of returns per unit time ij and the riskless borrowing-lending rate r, are all non-stochastic functions of time.

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35 3. Each investor maximizes his strictly con cave and time-additive utility function of consumption over his lifespan. Investors have homogenous exp ectations regarding the opportunity set. Let (,)Fxt denote the market value of a project. The market value is completely specified by the state variable x and timet, and represents the time and risk-adjusted value of the stream of cash flows generated fr om the project. Let the change in the state variable x be given by dxudtdz (3-17) The drift u and variance 2 may have the general form (,)uuxt and 22(,) x t .Let dt denote the cash flow generated by the project in time interval (,)ttdt with(,) x t Then, the return on the project in the time interval (,)ttdt is the sum of the capital appreciation (,)dFxt and the cash returndt Assuming that the function (,)Fxtis twice differentiable w.r.t. x and atleast once differentiable w.r.t.t, ItoÂ’s Lemma can be used to expand (,)dFxt as 2(,) 2txxxxdFxtFuFFdtFdz (3-18) The rate of return on the project is 2(,)1 (,)2x txxxdFxtdtF FuFFdtdz FxtFF (3-19) with expected value per unit time p and covariance with the market per unit time pm given by 21 2ptxxxFuFF F (3-20)

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36 x pmmF F since pmpm x mp mF F where (,) x t is the instantaneous correlation coefficient between dz and the return on the market portfolio. By substitution in the Equation 3-16 we obtain the PDE 21 ()0 2xxxtFuFFrF (3-21) First, it may be noted that is the correlation coefficient between dzand the return on the market portfolio. Since dzis the only source of stochast icity in the project and the underlying, is also equal to the correlation coefficient between return on the underlying and the return on the market portfolio. Second, when compared with the DP formulation using the discount rate it can be seen that the CC analysis modifies the total expected rate of return by a factor of which allows the use of the risk-free rate of returnr. In this manner the CC analysis converts the problem of valuing a risky asset to one of valuing its certainty equivalent. It does away with the need to determine the discount rate but does require an additional assumption regarding the completeness of the market or in other words only the marketed risk of the asset can be valued. Further, as shown in Hull and White (1988), if the state variable is a traded security and pays a continuous proportional dividend at rate then in equilibrium, the total return provided by the security in exce ss of the risk-free ra te must still be so that; r (3-22) or

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37 r (3-23) Substituting in Equation 3-21 we obtain the PDE derived by Dixit and Pindyck (1994) using the replic ating portfolio i.e., 21 ()0 2xxxtFrFFrF (3-24) Hull (2003) differentiates betw een the investment and the consumption asset. An investment asset is one that is bought or sold purely for the purpose of investment by a significant number of investor s. Conversely, a consumption asset is held primarily for consumption. Commodities like timber are c onsumption assets and can earn a below equilibrium rate of return. Lund and Oksendal (1991) discuss that generally investors will not like to hold an asset that earns a belowequilibrium rate of return. But empirically commodities that earn a below equilibrium rate of return are stored in some quantities. To quote Lund and Oksendal (1991): In order to explain storage of commodities whose prices are below-equilibrium, it is assumed that the storers have an advant age from the storage itself. This is known as gross convenience yield of the commod ity. The net convenience yield (or simply the convenience yield) is defined as th e difference between the marginal gross convenience yield and the marginal cost of storage. (Lund and Oksendal 1991, p.8) If we assume a continuous proportional convenience yield then the assumption is completely analogous to an assumption of a continuous proportio nal dividend yield. Therefore, the can represent the continuous prop ortional convenience yield from holding the timber and the PDE will hold.

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38 The Lattice Estimation Models The Binomial Lattice Model In order to determine the holding value of the asset i.e., the value in the continuation region, it is necessary to solve the PDE. As it is not always possible to obtain an analytical solution, several numerical procedures have been devised. Amongst the popular methods for obtai ning a numerical solution are the lattice or tree approximations (that work by approximating the stochastic process) and the finite difference methods, explicit and implicit (that work by discretizing the partial differential equation). Monte Carlo simu lations and numerical inte gration are other popular techniques. This study uses the lattice approximation ap proach for its simplicity and intuitive appeal. Depending on the nature of the problem the binomial or higher dimension lattice models were used. The binomial approximati on approach is suitable for valuation of options on a single underlying st ochastic state variable and was first presented by Cox et al. (1979). For an underlying asset that follows a GBM process of the form dx dtdz x (3-25) where the drift and the variance 2 are assumed constant, the binomial approach works by translating the continuous time GB M process to a discrete time binomial process. The price of a non-divi dend paying underlying asset denoted by P is modeled to follow a multiplicative binomial generating process. The current asset price is allowed to either move up over the next period of length by a multiplicative factor u to uP with subjective probability p or fall by the multiplicative factor dto dPwith probability

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39 (1) p To prevent arbitrage the relation 1 urd must hold where rrepresents the risk-free interest rate. The asset price follows the same process in every period thereafter. Following Ross (2002) it can be shown that the binomial model approximates the lognormal GBM process as becomes smaller. Let iYequal 1 if the price goes up at time i and 0 otherwise. Then, in the first nincrements the number of times the price goes up is 1n i iY and the asset price would be 1 0 n Y i i n nu PPd d Letting t n gives 10t i iY t tP u d Pd Taking logarithms we obtain 1 0lnlnlnt t i iP tu dY Pd (3-26) The iY are independent, identically distribu ted (iid) Bernoulli random variables with mean p and variance(1) p p Then, by the central limit theorem, the summation 1 t i iY which has a Binomial di stribution, approximates a normal distribution with mean t p and variance (1)t p p as becomes smaller (and t grows larger). Therefore, the distribution of 0lntP P converges to the normal distribution as t grows.

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40 Following the moment matching procedure Luenberger (1998) shows that the derived expressions for the parametersu,d and p are 211 2 22 p (3-27) ue (3-28) de (3-29) The DP procedure for analysis of an option on an underlying asset that follows GBM process would proceed by using a binomial lattice parameterized by these expressions. The DP procedure would obtain the option value by r ecursively discounting the next period values using th e subjective probability value p and an externally determined discount rate. In contrast the contingent analysis pro cedure is illustrated using the replicating portfolio argument as follows. In addition to the usual assumptions of frictionless and competitive markets without arbitrage opportunitie s, as noted earlier, it is assumed that the price of a non-divide nd paying asset denoted by P follows a multiplicative binomial generating process. The asset price is allowe d to either move up in the next period by a multiplicative factor u or fall by the multiplicative factor d.If there exists an option on the asset with an exercise price of X then the present value of the option denoted by c would depend on the contingent payo ffs in the next period denoted by 00,ucMAXuPX and 00,dcMAXdPX where 0P denotes the current price of the asset.

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41 In order to price the option a portfolio consisting of one unit of the asset and munits of the option written against the asset is constructed such that the end of the period payoff on the portfolio are equal i.e., 00uduPmcdPmc (3-30) Solving for m we get 0()udPud m cc (3-31) If the end of the period payoff is equal th e portfolio will be risk free and if we multiply the present value of the portfolio by1r we should obtain the end of period payoff 00(1)()urPmcuPmc (3-32) or 0(1) (1)uPrumc c mr (3-33) Substituting Equation 3-31 for m in Equation 3-33 yields (1)(1) (1)udrdur cccr udud (3-34) Letting (1)rd q ud (3-35) where qis known as the risk-neutral probability, we can express the present value of the option as (1)(1)udcqcqcr (3-36)

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42 From Equations 3-28 and 3-29 we have ue and de To find the value of the risk-neutral probability q these values of u and dcan be substituted in Equation 3-35 to obtain 2(1)11 2 22r re q ee (3-37) Compared with the expression for the subjectiv e probability p in Equation 3-27, it can be seen that under the risk-neutral valuation the drift of the GBM process is replaced by the risk-free rater. In general, if the asset pays out a continuous proportional dividend then, under CC analysis the drift is modified tor (Equation 3-24). The corresponding risk neutral probability is 21 2 22 r q (3-38) For the treatment of previsible non-stochast ic intermediate cash flows (costs) with a fixed value ( ) the Equation 3-36 is modified to (1)(1)udcqcqcr (3-39) It is implicit that represents the discounted net present value of all such cash flows in the period. The CC procedure for a single period outlin ed above is easily extended to multiple periods and the option value is derived by recursively solving through the lattice. The Trinomial Lattice Model for a Mean Reverting Process For an asset that follows a MR process of the form

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43 _dxxxdtdz (3-40) the contingent claims PDE is given by 21 ()0 2xxxtFrxFFrF (3-41) where _()() x xx x is a function of the underlying asset x Hull (2003) describes a general two-stage pr ocedure for building a trinomial lattice to represent a MR pro cess for valuation of an option on a single underlying state variable. For trinomial lattice the state variable can move up by a multipleu, down by a multiple dor remain unchanged represented bym. Since MR processes tend to move back to a mean when disturbed, the trinomial latti ce has three kinds of branching. Depending on the current value of the state variable the next period movements can follow one of the three branching patterns ( ,,,,,,,, mddumduum) with associated probabilities. The parameters of the lattice are determined by matching the moments of the trinomial and MR processes. The procedure can be adapted for most forms of the MR process. Details of the procedure can be found in Hull (2003). The Multinomial Lattice Model for Two Unde rlying Correlated Stochastic Assets When the problem is to find the value of an option on two underlying assets with values that follow the GBM processes iiiiiidxxdtxdz 1,2i (3-42) which are correlated with instantaneous correlation coefficient given by ( i.e., they have a joint lognormal distribution) th e contingent claim PDE has the form

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44 111222 1 22222 111212221 1 2 21 2 2 0xxxxxx x xtxFxxFxFrxF rxFFrF (3-43) A multinomial lattice approach is used to value such options, also called rainbow options. The development of the multinomial pr ocess for the correlated assets is similar to that described for the binomial lattice with a single underlying asset. The parameters of the multinomial lattice are derived by matc hing the moments of the underlying asset value processes. Hull (2003) discusses alternate lattice parameterization methods developed for the multinomial lattice valu ation approach. This study uses the method discussed in Hull and White (1988). At each node on the lattice the assets can move jointly to four states in the next period. The resulting parameter values for a three dimensional lattice are summarized in Table 3-1. Table 3-1. Parameter values for a three dimensional lattice _______________________________________________ Period 1 state Risk neutral probability _______________________________________________ 1,2uu 0.25(1) 12, ud 0.25(1) 12, du 0.25(1) 12, dd 0.25(1) ________________________________________________ Here 22i i ir iue (3-44) 22i i ir ide (3-45) represent, respectively, the constant up and down movement multiplicands for asseti. Parameter i represents the volatility and i represents the dividend /convenience yield of asset i while rrepresents the risk-free rate and the size of the discrete time step

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45 CHAPTER 4 APPLICATION OF THE CONTINGENT CLAIMS MODEL Who is the Pulpwood Farmer? Before applying the CC model to the pulpwood farmerÂ’s dilemma, it is necessary to establish a mathematical description of a pulpwood farmer. For a commercial timber production enterprise, the choice of timber pr oduct(s) to be produced (or rotation length chosen) is guided by the prevailing and expected future timber market prices amongst other things. The following discus sion describes the role of relative timber product prices in this decision. A slash pine stand will produce multiple ti mber products over its life. For products that are principally differentia ted by log diameter, the early part of the rotation produces the lowest diameter products like pulpwood. As the rotation progresses the trees gain in diameter resulting in production of higher diameter products like sawtimber. Since individual tree growth rates vary there is no en bloc transition of the stand from the lower to a higher diameter product, but rather, for most part of the merchantable timber yielding rotation ages the stand would contain a mix of products with the mix changing in favor of the higher diameter products w ith increasing rotation age. The average pine stumpage price data seri es reported by Timber Mart South (TMS) for different timber products reveal that on an average the large diam eter products garner prices that are significantly higher than lower diameter pr oduct prices (Table 4-1). This implies that the value of merchantable timber in the stand increases sharply with rotation age from the combined effects of larger merc hantable yields and increasing proportion of

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46 higher diameter timber. More important, it al so implies that short rotation farming may be difficult to justify using the TMS reported prices. Table 4-1. Florida statewide nominal pine stumpage average product price difference and average relative prices (1980-2005) _______________________________________________________________________ Timber Products Average absolute Average relative price difference prices ($/Ton) ______________________________________________________________________ Sawtimber vs. CNS 7.09 1.35 CNS vs. Pulpwood 9.79 1.99 _______________________________________________________________________ Source: Timber Mart-South In general, the cultivation of early rotati on products is differentiable from that of the late rotation produc ts by the silvicultural choices. Hi gh density planting and absence of pre-commercial thinnings are some choices that could characteri ze the cultivation of pulpwood. For slash pine, the decision to plan t dense and not resort to pre-commercial thinnings limits the stand ownerÂ’s choices wi th respect to switching to higher diameter product farming by prolonging the rotation. For a general slash pine stand with two products (pulpwood and sawtimber) the results of price differentiation on the optimal Faustmann rotation age are shown in Table 4-2. The illustration us es a year 0 establishment cost of $120/acre, no intermediate cash flows, a 5% constant annual discount rate, a cutover site index of 60 and 600 surviving trees per acre (tpa) at age 2 with the Pienaar and Rheney (1995) slash pine growth and yield equations. Pulpwood was defined as merc hantable timber from trees with minimum diameter at breast height (dbh) of 4 inches up to a diameter 2 inches outside bark and sawtimber as trees with minimum dbh 8 inches to 6 inches outside bark. The undifferentiated single timber pr oduct price was assumed $10/ton.

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47 Table 4-2. The effect of timber product pr ice differentiation on optimal Faustmann rotation ___________________________________________________________________ Price difference Optimal rotation age Absolute Relative $/Ton Years ___________________________________________________________________ 0 1.0 21 5 1.5 23 10 2.0 25 20 3.0 27 30 4.0 28 ___________________________________________________________________ Similarly, Table 4-3 shows that it is the relative product prices (for the purpose of this study, relative product price was define d as the price of the late rotation product expressed as a proportion of th e price of the early rotation pr oduct) that are important to the determination of the optimal rotation changes. Table 4-3 maintains the absolute increments while changing the size of the re lative increments. For this illustration the initial common timber product market price was assumed to be $20/ton. Table 4-3. The effect of timber product rela tive prices on optimal Faustmann rotation _______________________________________________________________ Price difference Optimal rotation age Absolute Relative $/Ton Years _______________________________________________________________ 0 1.0 20 5 1.25 21 10 1.50 23 20 2.00 24 30 2.50 26 _______________________________________________________________ Economic theory has it that relative pric ing of goods is an important market signal which allows the efficient allocation of res ources. In the context of the timber stumpage markets, relative product pricing serves as a signal to the timber producers to produce (more/less of) one or the other timber product. In order to induce producers to increase

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48 the production of late rotation pr oducts (which involve greater investment and/or risk) the market must offer a higher relative price. Since relative prices are not constant, an investment decision based on these prices must consider an average or mean of relativ e prices over an appropriate period of time. A timber land owner who bases his investment decision on the averag e relative prices deduced from the prices reported by TMS w ould never choose the lower rotation ages associated with pulpwood farming (19-25 year s for slash pine from Yin et al. 1998). Yet pulpwood farming is chosen by substantial numbers of timber land owners. Certainly, the average relative price of produc ts could not be the only re ason for choosing the pulpwood rotation. The practice of pulpw ood farming with slash pine c ould be the result of several considerations. Short rotations are attractive in themselves for the early realization of timber sale revenues (capital constraint cons ideration). Other considerations like earning regular income from the sale of pine stra w in denser stands with no thinnings also influence the choice. However, the altern ate considerations do not diminish the importance of relative product prices. The stum page prices applicable to a particular stand can be vastly different from the average prices reported by TMS. Some stands can experience greater relative pric es at the same point in tim e than others. This varying relative prices experienced by stands can be easily explained by th e nature of timber markets. Once the maximum FOB price that a timber purchaser can offer is determined for a period of time, the stumpage price appli cable to prospective suppliers is determined by the cost of harvesting and transporting th e timber to the location of the purchaserÂ’s consumption/storage facility. From the average difference between TMS reported FOB and stumpage prices, it can be seen that these costs form a very high (as much as 2/3 for

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49 pulpwood) portion of the FOB price. At a point in time harvesting co sts may vary little from one stand location to another in a re gion but the transportation costs can vary significantly. For a pulpwood stand located clos e to a purchaser (pulp mill) the relative product prices would always be lower than thos e for other distant stands (say with respect to a sawtimber purchaser in the region), justifying the pulpwood farming decision. This argument implies that a significant number of th e stands located close to pulp mills would be choosing pulpwood farming and this should be empirically borne out. It also implies that once multiple products are considered there can be no single Faustmann rotation age that suits all even-aged single same specie sta nds even if their site quality was the same; rather, there would be a continuum of op timal rotation ages depending on the average relative stumpage prices applicable for the stand. For the present analysis it was assumed th at the slash pine pulpwood plantation was located close to a pulpwood purchaser (who was not expected to stop operations) resulting in experiencing low average (long ru n) relative prices. For sufficiently small average relative prices it may also justify treating the entire merchantable timber output, irrespective of diameter size, as pulpwood. Despite the location advantage, in the s horter run, a stand w ould still experience wide differences in relative product prices from fluctuating market conditions. In the present conditions where pulpw ood prices have been depressed for several years while other products have fared relatively better, th ere is a market signal in favor of higher diameter products to all sta nds irrespective of location. Therefore, the pulpwood stand under analysis would be experiencing higher than normal relative pr ices. This situation

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50 was analyzed as a multiproduct option problem though with modest relative prices as compared to TMS reported prices. The Return to Land in Timber Stand Investments This section deals with the calculation of return to timberland. Land serves as a store of value as well as a f actor of production. As a fact or of production used for the timber stand investment, land must earn a retu rn appropriate to the investment. For the pulpwood farmer’s harvest problem land rent is a cost that will be incurred if the option to wait is chosen. In the contingent claim anal ysis the land rent is modeled as a parameter observed by the decision maker and hence ha ving a known present value. If land is not owned but rented/leased the explic it portion of this return is in the form of rent charged by the renter/leaser. However, whether explicit or implicit, there is very little useful data available on either timber land values or l ease/rent values. Most decision makers do not have access to a reliable estimat e of even the present value of their timber land. This makes it necessary to determine the appropria te return to be charged to land for the purpose of the analysis. The following discussi on uses the term ‘timberland value’ to refer to the value of the bare land, unle ss it is specifically stated otherwise. In its report on large timberl and transactions in the US the TMS newsletter (2005) reports a weighted average tr ansaction price in 2005 for the southern US of $1160/acre. Smaller timberland transactions at $2000/acre or higher in Fl orida are routinely reported. A part of these valuations must arise from the value of the land itself while some of the balance could be for the standing trees (if any). Recent literatu re discusses other important sources of the valuation like high nontimber values in the form of leisure and recreation values etc. and expectations of future demand for alternative higher uses. Aronsson and Carlen (2000) st udies empirical forest land pr ice formation and notes that

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51 non-timber services, amongst other reasons, ma y explain the divergen ce of the valuation from present value of future timber sale incomes. Wear and Newm an (2004) discuss the high timber land values in the context of us ing empirical timberla nd prices to predict migration of forest land to alternate uses. Zhang et al. (2005) look at the phenomena of timber land fragmentation or ‘retailization’ through sales to purchasers looking for aesthetic/recreation values and its implications for forestry. Since this analysis assumes that timber sales is the only major source of value the appropriate timberland value is the value of bare land the present best use of which is timber farming and for which non-timber valu es are insignificant. Assuming that the market price of such timberland could be obs erved, the question is: Can this value be used for the purpose of analysis? Is informa tion on the traded price of bare timber land appropriate for analysis? Chang (1998) has proposed a modified vers ion of the Faustmann model suggesting that empirical land values could be used with the Faustmann op timality condition to determine optimal rotations. Chang (1998) di scusses a generalized Faustmann formula that allows for changing parameters (stumpag e price, stand growth function, regeneration costs and interest rate) from rotation to ro tation. The form of the optimal condition derived by him is 1() ()kk kkkkk kRT R TLEV t (4-1) Here, () R T is the net revenue from a clear-cut sa le of an even aged timber stand at the optimal rotation age T and is the required rate of return. The subscript k refers to the rotation. The condition is interpreted to m ean that instead of the constant LEV of the standard Faustmann condition, the discounted value of succeeding harvest net revenues

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52 (1 kLEV) must be substituted. Chang (1998) interpre ts this to mean that the market value of bare land existing at the time of taking th e harvest decision can replace the standard constant Faustmann LEV. But, if the observed la nd value is very high as compared to the LEV the RHS of the equation increases signifi cantly, resulting in a drastic lowering of the optimal rotation age T In the state of Florida, whic h is experiencing high rates of urbanization, it is not unusual to find timber land valued at several multiples of the LEV. Failure to account for non-market values like aes thetic or recreational values in the model alone may not explain the failure to observe the rotation shortening effect. Klemperer and Farkas (2001) discuss this effect of using empirical land values while using ChangÂ’s (1998) version of the Faustmann model. By definition, the value of any asset is the discounted value of net surpluses that it is expected to provide over its economic lifetime in its best use. This suggests that the market value of land may be differentiable into two parts. One part of the market value is derived from the current best use and the other is the speculative or expected future best use (Castle and Hoch 1982). This means that th e present market valu e of timber land, if known, does not provide information on the valu e in current use without the separation of the speculative value component. The critical fact is that the land rent ch argeable to current best use cannot exceed the expected net surplus in current best us e. No investor would pay a land rent higher than the net surplus he exp ects to earn by putting it to use. Using empirical land values could result in overcharging rents as the la nd values may be inflated by the speculative value component.

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53 This gives rise to the question: What about the opportunity cost to the speculative component of land value? Does the landowner lo se on that account? The answer is that if a parcel of land is being held by the landow ner despite its current market value being higher than its valuation in cu rrent best use, an investment or speculation motive can be ascribed to the landowner. Th e landowner treats the land not only as a productive factor in the timber stand investment but also as a speculative asset. The landowner could earn a capital gain over and above the value of future rents in the current best use by selling the land in such a market. If the investor chooses to hold the land, it is because he expects to profit from doing so. And this profit is in the form of expected capital appreciation. It is this expected capital appreciation that co mpensates the landowner for the opportunity cost on the speculative component of land value A formal derivation of this argument follo ws from economic theory. According to the economic theory of capital, in a competitiv e equilibrium, an asset holder will require compensation for the opportunity cost on the curr ent market value of a capital asset plus the depreciation cost for allowing the use of his asset (Nicholson 2002). Representing the present market price of the capital asset byP, the required compensation v will be () vPd (4-2) where is the percent oppor tunity cost and dis the assumed proportional depreciation on the asset value. When the asset market value is not constant over time the required compensation will be a function of time()vt. The present value of the asset would equal the discounted value of future compen sation incomes. At the present time t the present discounted value (PDV) of the compensation received at time s ( ts ) would be

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54 ()() s tvse and the present discounted value of all future compensation incomes would be ()()()()stts ttPDVPtvsedsevseds (4-3) Differentiating ()Ptwith respect to t and ignoring depreciation we have () ()()()()tstt tdPt evsedsevtePtvt dt (4-4) Therefore, () ()() dPt vtPt dt (4-5) So, the required compensation income at any time is equal to the opportunity cost on the current market value of the asset less th e expected change in the market value of the asset. The interpretation is that the ‘fair’ or competitive compensation for leasing an asset consist of both the interest cost as well as the expected change in the value of the asset. If the expected change in the value is positive the rental charges are decreased to that extent since the value appreciation compensates the as set owner for a part of the interest cost. The net compensation () vt is the opportunity cost for the asset value in its current best use. It cannot be more th an this cost as prospective re nters cannot afford to pay more as already argued. It cannot be less because a lower charge would tran sfer a surplus to the renter attracting competition amongst renters. Therefore, to find the amount to be appr opriated as return to timberland it is required that its value in current (best) use be determined and then the return would be given by the opportunity cost of holding the land in its current (best) use.

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55 The static Faustmann framework determines the timber land value or Land Expectation Value (LEV) as the present va lue of net harvest re venues arising from infinite identical rotations in timber farming us e. In contrast, in the stochastic framework, the ability to actively manage the investme nt adds an option value which must be incorporated in the valuation method. As argued and shown by Plantinga (1998), Insley (2002, 2005), and Hughes (2000) a price responsi ve harvest strategy adds a significant option value to the investment. In this study the land value was determined within the CC analysis assuming that timber farming was the current best use. The parameters for the valuation are the current values of timber price and pl antation establishment cost as well as annual maintenance costs. In the risk neutral anal ysis the current risk-free rate se rves as the discount rate. The infinite identical rotations methodology was used to capture the tradeoff with future incomes meaning that the net expected surplu s value over the first rotation was used as the expected average value for future rota tions. This may not effect the land value significantly since, as observed by Bright and Price (2000), the present value of net surplus in the first rotation forms most of (>80%) the estimated timberland value when calculated in this way, for a sufficiently l ong rotation and high disc ount rate. Therefore, the land value in current use can be estimated with information available to the decision maker. And the land rent is the opportunity cost of this value. The mathematical formulation of the land value estimati on problem is given by Equation 4-6. 1 00 0|() max 1t rtri qti i rt tEPPQteaeC LV e (4-6) Here,

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56 L V= Present value of land q E = Risk neutral expectation operator tP = Stochastic timber price at t period from present time 0 ()Qt= Deterministic merchantable timber yield function (of rotation age t ) ta = Annual recurring plantation administrati on expenses treated as risk-free asset 0C = Value of plantation establishment expens es to be incurred today (at year 0) r = Present risk-free interest rate assumed constant in future Then, the present value of estimated land rent isrLV. There are some points to note about the above argume nts and methodology outlined for determining the land value in current best use. First, it is implied that the rent value is calculated afresh by the decision maker every period. This is empirically true for shorter duration uses like agri culture farming and there is no reason why it should not be so for timber farming if decision makers are e fficient information processors as normally assumed and information is easily and freel y accessible. If market information on comparable land rents was available, it would be stochastic and th e decision maker would utilize the new information availabl e every period for decision making. It is also important that a stochastic rent value calcul ated as argued above captures and transfers fresh information about the exp ected future to the decision making process. That is, if the estimated land/rent value is high, it will increase the cost of rotation extension and vice versa. For example, if the stand owner learns of a demonstrable technological advance improving the financial returns to stan d investments, in the midst of the rotation, the stand owne r will seek to apply the techno logy to the present rotation, thereby adopting the ‘best use’. However, if the improved technology cannot be applied to the current rotation then there should be pressure on the decision maker to shorten the present rotation so that the improved technology could be applied to the next rotation.

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57 There is no empirical evidence known to suppor t this result regarding timberland owner behavior but it can be argued that timberla nd owners never have free and easy access to the necessary information. It is also importa nt to note that market prices of timberland themselves provide no valuable input to the st and decision making proces s but rather it is the value in current best use that is relevant Thus, in periods of speculative inflation of land prices one may not expect to observe any appreciable change s in stand decision making behavior. It is the direction of change s in real input costs and output values which result in changes in land rent. Second, it is implied that the rent value is a function of the current timber price(s). However, it need not be perfectly correlated with timber prices(s) since other parameters of the valuation (the costs and discount ra tes) would be expected to follow (largely) independent stochastic processes. Third, the method outlined for estimation of land rent provides an estimate for a single period i.e., for the present period only. Ideally, the rent value should be modeled as a stochastic variable. But that would require information rega rding the stochastic process defining the plantation expenses (or non-timber sources of ca sh flow) and discount rate. In the absence of data on the stochastic process for the other flows, in the following analysis, land rent was assumed to behave like a risk-free asset. On the Convenience Yield and the Timber Stand Investment To solve the harvesting problem using the la ttice approach the c onstant volatility of the underlying variable is estimated from hist orical price data. Th e risk-free rate is estimated from yields on treasury bills of matching maturity. However, the estimation of the convenience yield poses a problem. The concept of convenience yield, as it is popularly interpreted, was first propos ed by Working (1948, 1949) in a study of

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58 commodity futures markets. The phenomena of “prices of deferred futures….below that of the near futures” (Working 1948, p.1) wa s labeled an inverse carrying charge. The carrying charge or storage cost is the cost of physically ho lding an asset over a period. The concept can be illustrated as follows: ignoring physical storage costs, the arbitrage free forward price F for future delivery of a commodity is determined by the relation rt oPe where 0P is the current unit price of the commodity, ris the borrowing/lending rate while t is the period of the contract. Therefore, the forward price should be proportional to the length of the contract. The inverse carrying ch arge or convenience yield discussed by Working (1948, 1949) is said to accrue to the contract writer when the no arbitrage relation does not hold for some contract lengths andrt oFPe Even though an opportunity for arbitrage exists, arbitrag eurs are unable to take advantage as nobody that is holding the commodity in inventory is willing to lend the commodity for shorting. Inventory holder may be unwilling to le nd the commodity when markets are tight (Luenberger, 1998) i.e., supply shortage is anticipated. Brennan (1991) defines convenience yield of a commodity as: ...the flow of services which accrue to th e owner of a physical inventory but not to the owner of a contract for future delive ry. ....the owner of the physical commodity is able to determine where it will be stored and when to liquidate the inventory. Recognizing the time lost and the cost s incurred in ordering and transporting a commodity from one location to another, the marginal convenience yield includes both the reduction in costs of acquiring i nventory and the value of being able to profit from temporary local shortages of the commodity through ownership of a larger inventory. The profit may arise from either local price variations or from the ability to maintain a production process despite local shortages of a raw material. (Brennan 1991, p.33-34) The convenience yield is not constant but would vary with the gross inventory of the commodity in question, amongst other things.

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59 If there exists a futures market for the co mmodity then the futures prices represent the risk neutral expected values of the commodity. The risk neutral drift() t which will be a function of time since the convenience yield () t and forward risk free rate () rt are empirically stochastic, can be calculated from the futures prices as (Hull 2003) ln() (1) ()ln () Ft Ft t tFt (4-7) Here, () Ft is the futures price at time t. In the absence of a futures market, theore tically it should be possible to estimate the convenience yield by comparing wi th equilibrium returns on an investment asset that spans the commodityÂ’s risk (replicating portf olio).As discussed by McDonald and Siegel (1985), the difference between the equilibrium rate of return on a financial asset that shares the same covariance as the asset and expected rate of return on the asset will yield But, empirically, such an asset is diffi cult to locate or construct from existing traded assets. Similarly, we could estimate r from the equivalent Usually, the Capital Asset Pricing model (CAPM) for timbe r stands is estimated by regressing excess returns on the historical timber price agains t the excess returns on the market portfolio. Thus, this methodology suffers from the failure to incorporate the convenience yield in total returns on timber stands. Using the estimated by this method will only yield r i.e., a value of zero. To the best of this authorsÂ’ knowledge no method for estimating convenience yield for timber is available in published li terature. Therefore, this study proceeds by assuming that the convenience yield0 and r The results are tested for sensitivity to different levels of constant

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60 Dynamics of the Price Process Modeling the empirical price process is th e key to the development and results of the real options analysis. Beginning with Wa shburn and Binkley (1990a) there has been debate over whether the empiri cal stumpage price returns pr ocess is stationary (meanreverting) or non-stationary (random walk). Th e debate has remained inconclusive due to the conflicting evidence on the distorting eff ect of period averaging on prices. Working (1960) was the first to show that the first differences of a period averaged random chain would exhibit first orde r serial correlation of the magnit ude of 0.25 (approximated as the number of regularly spaced observations in the averaged period increased). Washburn and Binkley (1990a) found consis tent negative correlation at the first lag for several quarterly and annual averaged stumpage pri ce series though most were less than 0.25 and statistically significant only for prices in one case. On the other hand, Haight and Holmes ( 1991) have provided heuristic proof to the effect that a stationary first order autoregr essive process, when averaged over a period, would behave like a random walk as the size of the averaging period was increased. They used this proof to explain away the observed non-stationarity in the quarterly averaged stumpage prices. The stationarity of the price process has im plications for the efficiency of stumpage markets. “A market in which prices always “fully reflect” available information is called “efficient”” (Fama 1970, p.383). U tilizing the expected rate of return format, market efficiency is described as ~~ ,1,1 ,|1|jtjt ttjtPrP (4-8)

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61 Here, E is the expectation operator, ,jtP is the price of security j at time t ~ ,1 jtP its random price at time 1 t with intermediate cash flows reinvested, ~ ,1 jtr is the random one-period percent rate of return ~ ,1 , jt jt jtPP P, t represents the information set assumed to be fully reflected in the price at t The information set t is further characterized accordi ng to the form of efficiency implied i.e., weak form efficiency which is lim ited to the historical data set, semi-strong form efficiency which includes other publicly available information and strong form efficiency that also includes the privat ely available information. As Fama (1970) discusses, the hypothesis that asset prices at any point fully reflect all available information is extreme. It is more common to use historical data to test prices for weak form efficiency in support of th e random walk model of prices. Washburn and Binkley (1990a) tested for the weak form efficiency using the equilibrium model of expected returns with alternate form s of SharpeÂ’s (1963) singleindex market models. Ex-post returns to st umpage were regressed on a stock market index and an inflation index. The residuals from the regressions were then tested for serial correlation the presence of which woul d lead to rejection of the weak form efficiency hypothesis. Since these tests requ ired the assumption of a normal distribution for the residuals, this was tested using th e higher moments (skewness and kurtosis). The non-parametric turning points test was also conducted as an alternate test for serial dependence. They found evidence of stationa rity in returns generated from monthly averaged data but returns generated from quart erly and annually averaged data displayed

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62 non-stationarity. Sign ificantly, they did not find ev idence to support the normal distribution assumption of the residuals. Haight and Holmes (1991) used an Au gmented Dickey-Fuller test and found stationarity in instantaneous returns on monthly and quarterly spot stumpage prices and non-stationarity in instanta neous returns on quarterly averaged stumpage prices. Hultkrantz (1993) contended th at the stationarity found in returns generated from monthly averaged price series by Washburn and Binkley (1990a) could be consistent with market efficiency when producers were risk averse. He used a panel data approach to Dickey-Fuller tests and found that southern st umpage prices were stationary. Washburn and Binkley (1993) in reply ar gued that the results of Hultk rantzÂ’s analysis were by and large similar to their analysis and point out th at if Haight and Holmes (1991) proof of the behavior of averaged prices is consider ed, then both (Hultk rantz 1993 and Washburn & Binkley 1990) their analyses could be biased away from rejection of the weak form market efficiency. Yin and Newman (1996) used the Augmented Dickey-Fuller and arrived at conclusions similar to HultkrantzÂ’s (1993). Gjolberg and Guttormsen (2002) applied the va riance ratio test to timber prices to check the null hypothesis of a random walk fo r the instantaneous returns. Their tests could not reject the random walk hypothesis in the shorter periods (1 month and 1 year) but over longer horizons, they f ound evidence of mean reversion. Prestemon (2003) found that most southern pi ne stumpage price series returns were non-stationary. He noted that te sts of time series using altern ate procedures may not agree regarding stationarity or market informational efficiency as time series of commodity asset prices may not be martingales.

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63 McGough et al. (2004) argue that a first or der autoregressive process for timber prices is consistent with efficiency in th e timber markets. They advocate the use of complex models (VARMA) that include dynami cs of the timber inventory while noting that such models would be difficult to estimate and apply to harvesting problems. In summary, in the absence of better data and models or stronger tests, it is difficult to conclusively establish the efficiency or otherwise of stumpage markets and or choose between the random walk or autoregressive models. This study considered both, the stationary and non-stationary models, for the price process alternately. Modeling the Price Process Two alternate models for the stochastic price process were applied to the real options model. The first model is the Geom etric Brownian motion which a form of the random walk process that incorporates a drif t and conforms with the efficient market hypothesis. Expressed mathematically it is dPPdtPdz (4-9) Here, P=Price of the asset at time t = Constant drift = Constant volatility dz= Increment of a Weiner process Geometric Brownian motion processes tend to wander far away from their starting points. This may be realistic for so me economic variables like investment asset prices. It is argued th at commodity prices (Schwartz 1997 ) must be related to their longrun marginal cost of production. Such a sset prices are modeled by Mean Reverting processes, which is the second model used fo r the stochastic process in this analysis. While in the short run the price of a comm odity may fluctuate randomly, in the long run

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64 they are drawn back to their marginal co st of production. The Ornstein-Uhlenbeck process is a simple form of the MR process expressed as _dPPPdtdz (4-10) Here, = Coefficient of reversion _P = Mean or normal level ofP The is interpreted as the speed of reversion. Higher values of correspond to faster mean reversion. _P is the level to which P tends to revert. _P may be the long run marginal cost of production .. The expected change in Pdepends on the difference between Pand_P If Pis greater (less) than_P it is more likely to fall (rise) over the next short interval of time. Hen ce, although satisfying the Mar kov property, the process does not have independent increments. The Weiner process in discre te time is expressed as ttttzzt (4-11) Here, t = Realization of a Normal Random variable with mean 0 variance 1 and (,)0ttjCov for 0 j In continuous time, the process is ttdzdt. A Weiner process tz is a random walk in continuous time with the properties (Luenberger 1998) i. For any st the quantity ()() z tzs is a normal random variable with mean zero and variance ts ii. For any 12340 tttt the random variables 21()() ztzt and 43()() ztzt are uncorrelated.

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65 iii. 0()0 zt with probability 1. The Geometric Brownian Motion Process Applying ItoÂ’s lemma, the Geometric Br ownian motion (GBM) process can be expressed in logarithmic form as 2ln 2tdPdtdz (4-12) In discrete logarithmic form the equation becomes 2 1lnln 2ttPPtz (4-13) Thus, the log-difference or the instantane ous rate of price change is normally distributed. In order to model the GBM process an es timate of the volatility was required. Following Tsay (2002), let1lnlntttrPP Then, tr is normally distributed with mean 21 2 t and variance2t where t is a finite time interval. If rs denotes the sample standard deviation i.e., 2 11n t t rrr s n (4-14) then ^ rs t (4-15) Here ^ denotes the estimated values of from the data. For the nominal F.O.B. and stumpage statewide pulpwood quarterly pr ice data for Florid a, using the above methodology we obtain the estimates listed in Table 3-4.

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66 However, as the TMS data is availabl e in period average form while the GBM process models the behavior of spot prices, it is necessary to account for any distortion to the statistical properties of the data from averaging. Working (1960) has demonstrated that to an approximation, the variance of ra tes of change calculated from arithmetic averages of n consecutive regular spaced valu es of a random chain will be 2 3 of the variance of first difference of correspondingly positioned terms in the unaveraged chain, as n increases. The prices reported by TMS are calc ulated as an arithmetic average of all reported prices in a quarter. As discusse d by Washburn and Binkley (1990b), the price averages will be unbiased estimates of the arithmetic mean of prices at anynregular intervals within the period so long as the likelihood of a timber sale occurring and the expected transaction size are cons tant throughout each period. Making the necessary correction to the es timated variance we obtain the revised estimate of the variance listed in Table 4-4. Table 4-4. Estimated GBM process paramete r values for Florida statewide nominal quarterly average pulpwood prices ______________________________________________________________________ Estimated Parameter FOB Stumpage ______________________________________________________________________ Uncorrected Values Standard Deviation 0.10 0.24 Corrected Values Standard Deviation 0.12 0.29 ______________________________________________________________________ It may be noted that the calculated sta ndard deviation for the F.O.B. price was significantly lower than the st andard deviation of the st umpage price. One possible explanation is that pulp mills revise their mill delivered prices relatively infrequently, whether they are gate purchase prices or supplie r contracted prices. It is also possible that while gate purchase prices are public knowledge, mill delivered price of pulpwood purchased from other sources may be incompletely reported due to mill concerns with

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67 strategic competitive disadvantage from revealing prices. On the other hand harvesting and transportation costs change drastically from one stand to another, resulting in higher volatility of reported stumpage prices. The st umpage prices reporte d are not the prices experienced by a particular stand or a common price experienced by all stands but prices experienced by different stands that reported selling timber in the period. The harvesting and transportation costs are themselves vola tile and likely imperfectly correlated with FOB prices but it is possible that they do not account for th e entire difference in the reported standard deviations. Since stand ow ners experience the stumpage price and not the FOB price, in the absence of data on volat ility of harvesting and transportation costs, this study uses the estimated standard deviat ion of reported stumpage prices to replace the estimated volatility of F.O.B. prices wh ile treating the harvesting and transportation costs as non-stochastic. To account for the pos sibility of overestimation of timber price variance the analysis was subjected to te sts of sensitivity to price volatility. Statistical Tests of the Geom etric Brownian Motion Model The GBM process in discrete logarithmic fo rm is a discrete random walk with drift i.e., it has the general form 1tttyy where 2(0,)tNt and (,)0ttjCov for 0j (4-16) The process is clearly non-st ationary with a unit root. But if we take the first difference we obtain a stationary process tty (4-17) The first difference process has mean and variance2t Further, the covariance (,)0ttsCovyy for 0 s

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68 To see how well the empirical data fits the GBM model, the sample autocorrelation function (ACF) at several lags was calculated and plotted for the di fferenced logarithmic form of the price data series. The sample ACF () h at lag hwas calculated using the formula || __ || 1 ^ 2 1()nh tht t n t tyyyy n h yy n nhn (4-18) where 11n t tyy n is the sample mean. For largen, the sample autocorrelations of an independent identically distributed (iid) sequence with finite variance ar e approximately iid with distribution 1 (0,) N n (Brockwell and Davis 2003). Hence, for the iid sequence, about 95% of the sample autocorrelations should fall between the bounds 1.96 n For the GBM process the instan taneous rate of price change1lnlntttrPP is stationary with mean 21 2 t variance2t and covariance (,)thtCovrr equaling zero for all 0 h If the empirical price data is m odeled by the GBM process, then the sequence tr should be white noise i.e., it shoul d be a sequence of uncorrelated random variables. A plot of the sample autocorrela tions for the instantaneous rate of price changes of the reported nominal pulpwood statew ide stumpage prices along with the 95% confidence intervals are presented in Figure 4-1, plotted using th e ITSM 2000 statistical software (Brockwell and Davis 2003).

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69 -1.00 -.80 -.60 -.40 -.20 .00 .20 .40 .60 .80 1.00 0 5 10 15 20 25 30 35 40 Sample ACF Figure 4-1. Sample autocorrelation function plot for nominal Flor ida statewide pulpwood stumpage instantaneous rate of price changes The dashed lines on either side of the cen tre plot the 95% conf idence interval. If the sequence is stationary, for the 40 lags plotted, 2 or less ACFÂ’s should fall beyond the 95% confidence bounds. For the stumpage price series, no more than 1 ACF beyond lag 0 fell outside the 95% confidence bounds. Signifi cantly, as proved by Working (1960), the ACF at lag 1 for stumpage price instantaneous rate of change sequence was approx. 0.25 (^(1)0.27 ). This could be the effect of the averaging process. To check if the trsequence was Gaussian i.e., if all of its joint distributions are normal the Jarque-Bera test was used. The Jarque-Bera test statistic is given by (Brockwell and Davis 2003) 2 4 2 2 2 3 3 23 624 m m m n m distributed asymptotically as 2(2) (4-19)

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70 if 2 (,)tYIIDN where 1r j n r jYY m n The results of the Jarque-Ber a test applied to the stumpa ge prices, given in Table 45, indicate that the normality hypothesis wa s weakly supported by the empirical data at the 5% level of significance. Table 4-5. Results of Jarque-Bera test a pplied to GBM model fo r Florida statewide nominal quarterly average pulpwood stumpage prices _______________________________________________________ Test Value Stumpage _______________________________________________________ Jarque-Bera Test Statistic 6.0984 P-value 0.0474 _______________________________________________________ The ACF test indicates that the GBM pro cess can be used to model the empirical data. However, this does not exclude the possibility of the true price process having a non-constant drift and/or variance. Lutz (1999) tested stumpage price series for constant variance. He found that the vari ance for stumpage price series examined was not constant for the early parts of the series i.e., up to 1920. From 1920 onwards, the examined series were found to display constant variance. Even if stumpage price data is heteroskedastic, it was implicitly assumed that the logarithmic transformation rendered the data ho mescedastic. The test results also did not exclude the possibility of an alternate model providing a better fit. On the basis of the ACF test it was only possible to conclude that there was insufficient evidence to reject the GBM model. The Mean Reverting Process The simple MR process is given by

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71 _ttdPPPdtdz Hence, tP is normally distributed with 00__ |t tEPPPPPe and 2 02 var|1 2t tPPe Using the expected value and variance we can express tP as 2 0_ 1 10,1 2t tt te PPePeN 1_ 1tttttPPPePPe 1_ttttPPPP when 1 e (4-20) where 2 0,tN The last equation provides a discrete time first order autoregressive equivalent of the continuous time Ornstein-Uhlenbeck proc ess. In order to estimate the parameters an OLS regression of the form 1 ttttPPabP (4-21) with aP and b was run. Then, the estimated parameters are given by ^ ^ ^a P b ^^b and ^ One problem with using this simple form of the MR process is that it allows negative values for the stochastic variable. Pl antinga (1998) justifies the choice of this model for timber stumpage price by referr ing to the possibility of harvesting and transporting costs exceeding the FOB timber pric e. In such a case the effective stumpage price would be negative. However, it can be argued that the ne gative stumpage price

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72 would still be bound by the harvesting and transportation costs i.e., if the FOB price were zero, the negative stumpage pr ice cannot be larger than the cost for harvesting and removal. But the MR process described abov e is unbounded in the negative direction. Hull (2003) describes an alternate lo g normal form of the MR process _lnlnlnttdPPPdtdz (4-22) This model restricts the price process to positive values. Thus, lntP given 0ln P is normally distributed with mean __ 0lnlnlntPPPe and variance 22 1 2te The deviations from the long-run mean are ex pected to decay following an exponential decline. This analysis uses this form of the MR process to model the FOB prices. When the harvesting and transportation costs are dedu cted from the stochastic values of the FOB price the magnitude of resulting negative stumpage price is restricted to these costs. However, adopting Equation 4-21 for estimation of _P implies that the mean to which the process reverts is constant over any period of time. Considering that _P is interpreted as the long run cost of production, over a short interval of a few days or weeks, it may be feasible to assume that the value is constant. But when the analysis must cover several years, this assumption is questionable. One common correction method employed (Smith and McCardle 1999) is to re gress the inflated values (present) of the historical asset price, which yields an inflation adjusted estimate of_P It also implies that for an analysis conducted in nomina l terms, the future values of _P must be inflated at an estimated average inflation rate. The average rate of annual inflation computed from the PPI (1921-2005) was approx.3.0%.

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73 The possibility of a constant real or inflation adjusted nominal_P for pulpwood prices was corroborated by the historical performance of pulpwood prices over the 30 years or so of TMS reporting period as we ll as the RPA (2003) projections of future performance. This phenomenon can be partly attribut ed to technological advances and in some measure to adverse demand and supply movements. Considering that the other parameter estimates are only marginally effected this analysis uses the inflation adjusted parameter values. To check for the effect of period averaging on the estimated parameter values, simulation was carried out. The simulation re vealed that regression of period averaged data generated consistent estimates of _P while was consistently underestimated by a factor of approx. 0.67 or the Working’s correc tion. The result of regressing inflated past values of the pulpwood prices using the Pr oducers Price Index-All Commodities (PPI) on estimated _P and other parameters are listed in Table 4-6. Table 4-6. Inflation adjusted regressi on and MR model parameter estimates _____________________________________________________________________________ ^a ^b ^ _P ^ ^ _____________________________________________________________________________ Stumpage Price 0.2979 -0.1245 2.3922 0.1245 0.2230 Standard Error 0.3285 0.1278 FOB Price 0.5694 -0.1781 3.1963 0.1781 0.0945 Standrad Error 0.4338 0.1350 _____________________________________________________________________________ Estimates corrected for period averaging effect. Of particular importance are the reversion coefficient values. For both price series the reversion coefficient values are low indicat ing that the annual price series exhibit low or insignificant reversion behavior. The ‘half life’ of the MR process or the time it takes

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74 to revert half way back to the long run mean, given by ln0.5 was approximately 2.6 years for stumpage price, illustrating the extr emely slow reversion process. Regarding the low values of mean reversion coefficients Dixit and Pindyck (1994) observe that this seems to be the case for many economic variable s and that it is usually difficult to reject the random walk hypothesis using just 30 or 40 years data. Secondly, the estimated variance for the FO B price process was sharply lower than that for the stumpage price. Once again, this difference can be attributed to the stochastic harvesting and transportation costs but may also partly be the result of the unsuitable data. As in the GBM process case, the an alysis was conducted by attributing the stumpage price process variance to the FOB price process and usi ng a non-stochastic harvesting and transportation cost. Finally, it must be noted that the lattice mo del for MR process used in this analysis was based on the existence of futures mark ets for the commodity and hence knowledge of futures prices, which represent the risk neutra l expected future values. In the absence of futures markets for timber, the value of the inflation adjusted estimated _Pwas used. This was justified for a long interval since mean re verting prices (and henc e futures prices) are expected to converge to _Pin the long run. However, in the short run this only serves as an approximation. Statistical Tests of the Mea n Reverting Process Model Examination of the stumpage price regres sion residuals shows first order serial correlation (Figure 4-2) as shown by Working (1964).

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75 -1.00 -.80 -.60 -.40 -.20 .00 .20 .40 .60 .80 1.00 0 5 10 15 20 25 30 35 40 Sample ACF Figure 4-2. Sample autocorrelation function plot for nominal Flor ida statewide pulpwood stumpage price MR model regression residuals Also, the results of Jarque-Bera test for normality do not support the normality hypothesis (Table 4-7) Table 4-7. Results of Jarque-Bera test ap plied to MR model residuals for Florida statewide nominal quarterly aver age pulpwood stumpage prices _______________________________________________________________ Test Statistic Stumpage Price _______________________________________________________________ Jarque-Bera Test Statistic 9.6299 P-value 0.0081 _______________________________________________________________ Instantaneous Correlation In order to model the simultaneous stochas tic evolution of two correlated stochastic processes following the GBM, an estimate of the instantaneous correlation between the two time series was required. The estimation of the instantaneous correlation of two period averaged GBM processes is not eff ected by period averaging (Appendix). The estimated instantaneous correlation for th e TMS reported Florida statewide average stumpage quarterly prices of pulpwood an d chip-n-saw assuming GBM processes was 0.43.

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76 The Data Time series data on prices of the timber products was acquired from Timber MartSouth (TMS). Price data for Florida extending from the last quarter of 1976 for pulpwood and the first quarter of 1980 for chip-n-saw to the second quarter of 2005 are used for the analysis. The stumpage price data are re ported by Timber Mart-South as quarterly average of final sale prices recorded in auctions for timber products in the reporting region. The data was used to represent spot timber prices in the analysis. However, due to the nature of data generation, collection and reporting processes, the validity of the data for this purpose is suspect. For example, the process starting from bidding for the timber to removal of the timber from the stand is usually a few months long. This means that the auction bid prices are a reflection of the bi dderÂ’s expectation rega rding future prices when the timber will actually reach the ma rket, not the immediat e price. Errors in recording, approximations etc also undermin e the data. Other shortcomings have been discussed in various contexts above. Harvesting and transportation cost was calculated using the difference between reported F.O.B. and stumpage prices of tim ber. The appropriateness of this method is questionable because of the time difference between auctions and actual movement of timber from the stand to the market. Growth and Yield Equations Slash pine growth and yield equations deve loped by Pienaar and Rheney (1996) are used. These equations for cutover forest land were developed using da ta from plantations sites in Georgia. The average site index for the sites was 60 ft (at age 25). The equations used in the analysis are

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77 i. Expected Average dominant height (Hin ft) 0.073451.804 0.0691 123131.3679(1)(0.6780.5461.3950.412)* A ge AgeHSeZZZZZAgee where S = Site Index 1 Z =1 if fertilized, zero otherwise 2 Z =1 if bedded, zero otherwise 3 Z =1 if herbicided, zero otherwise ii. Survival after the second growing season (in trees/acre) 1.3451.345 210.0041() 21 AgeAgeNNe where 1Nand 2N are trees per acre surviving at 1Age and 2Age respectively21()AgeAge iii. Basal area ( B in ft2 /acre) 35.6686.2053.155 3.3941.3360.366 0.09 1312 (0.5570.4362.1340.354)*AgeAgeAge Age BBeHN ZZZZZagee where B Z = 1 if burned, zero otherwise iv. Stem Volume outside bark (V in ft3/acre) 0.320.501 0.0171.016 0.82 A geAgeVHNB v. Merchantable volume prediction (, dtV in ft3/acre) 3.845.72 0.120.520.69 td N DD dtVVe where ,dtV=per acre volume of trees with dbh>d inches to a merchantable diameter t inches outside bark D = quadratic mean dbh in inches 0.005454 B N = pi

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78 The merchantable yield output from the gr owth and yield models is in units of ft3 outside bark/acre. To convert the yield to tons/acre conversion factors of 90 ft3/cord and 2.68 tons/cord (Timber-Mart South) were used. These equations were developed from experimental plantations reaching an age of 16 growing seasons. For this reason, their us e for extrapolating growth and yield to higher rotation ages is questionable (Yin et al.1998) and may not represent the true stand growth. Nevertheless, for the purpose of this analysis, these equations are the best source for modeling the growth and yield of slash pine. Plantation Establishment Expenses Average plantation establishment expenses for cutover land in the US South reported by Dubois et al. (2003) were used. Th e relevant reported costs are listed in Table 4-8. Table 4-8. Average per acre plantation establishment expenses for with a 800 seedlings/acre planting density _______________________________________________________________________ Expense Head 2002 2005 ________________ ________________ $/acre $/seedling $/acre $/seedling _______________________________________________________________________ Mechanical Site preparation* 166.50 195.82 Burning** 15.02 17.66 Planting cost* 49.99*** 58.79 Seedling cost 0.04 0.05 _______________________________________________________________________ Total Cost for 800 seed lings/acre 280. 00 329.21 _______________________________________________________________________ All Types ** Others *** Planting cost for average 602 seedlings/acre The Producers Price Index-All Commodities was obtained from the Federal Reserve Economic data and used to extrapolate the nominal plantation expense data

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79 reported for 2002 to 2005. The index stood at 132.9 in December 2002 and rose to 156.3 by July 2005. For an acre planted with 800 s eedlings the total planting cost under the above listed expense heads in July 2005 was estimated at $329.21 or approx. $330/acre. Risk-Free Rate of Return The yield on Treasury bills with 1 year maturity (Federal Reserve Statistical Release) was used as the estimated risk-free ra te of return. The reported risk-free rate for July 2005 was 3.64%. The Model Summarized The value of options available to the d ecision maker were analyzed using a CC valuation procedure. The analysis also highlig hted the form of the optimal strategy. The following are the important points of the model 1. The model considers an even-aged mature (20 year age) slash pine pulpwood plantation in 2005. Only revenues from sale of timber are considered significant for the analysis. Since the analysis focuses on the pulpwood crop, the plantation was assumed to have been planted dense (800 tr ees/acre initial planting density) with no thinnings up to the present age. The planta tion was assumed to be cutover with site index 60 ft (rotation age 25). Site preparat ion activities assumed are mechanical site preparation (shear/rake/pile) and burning only. A clear-cut harvest was considered for the final harvest. When the thinning opti on was the subject of analysis only a single thinning in the form of a row thinning that removes every third row of trees was considered. 2. For a stand with the chosen initial planting density and site index on a cutover site, the growth and yield equations produce a single product yield curve that peaks approximately at age 43. Rotation age 43 was selected as the terminal age for the options on the stand in this study. This terminal age was applied uniformly to all models for comparability of results. Even though later stand products would have later yield peaks, current empirical practi ces and unreliability of the yield curve for higher rotation ages were arguments in favor of the lower terminal age. 3. Only the timber price (prices for multiple product analysis) was modeled as a stochastic variable. It was assumed that th e stand growth and yield models provide a reliable forecast of the future merchantable timber yields. 4. The present values of other parameters of the valuation model like the risk-free interest rate, the land rent and the interm ediate expenditure/cash flows on plantation

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80 were assumed known to the decision maker. Some intermediate cash flows could be positive in the form of regular realizatio ns of amenity values or sale of some minor/non-timber products while others coul d be negative in the form of annual taxes and overhead expenditures associated with maintenance activities. The basic analysis assumes that the net result from a combination of both positive and negative cash flows was a negative cash flow of $10/acre/year. For the purpose of consistency, all intermediate cash flows are treated as occu rring at the beginning of a period. This arrangement does not effect the analysis since the intermediate cash flows are assumed non-stochastic. Nonstochastic variables are una ffected by the expectation operator but are effected by discounting. So, regardless of where they occur in the period their value at the beginning of the period can be considered as the appropriately discounted value. These known values were extrapolated like risk-free variables i.e., with close to zero varian ce (and no correlation with the stochastic variables). Harvesting and transportation costs per unit merchantable timber were assumed constant i.e., the effect of economi es of scale observed for older or larger stands was ignored for want of data. 5. The unit FOB price of the timber product was modeled as the stochastic variable and the unit harvesting and transportation costs de duced from empirical data served as the strike price for the option on the stand. The estimated empirical values of variance for the timber stumpage price were used to model the variance of the FOB price. 6. The GBM and MR models applied were assume d to have constant parameters i.e., the drift and variance for the GBM model and the reversion coefficient and variance parameters for the MR models were assumed constant. 7. Land rent was estimated for the stochastic price process using the CC valuation as detailed earlier. 8. Taxes are not specifically treated in the analysis. 9. Ideally the term structure of interest rates should be used to model the risk-free rate. For simplicity, a single constant risk-free interest rate was used instead. 10. An assumption was made that the pulpwood stand was located so as to experience low/moderate relative timber product prices. 11. For the basic model the convenience yield was assumed to be zero. Sensitivity analysis to consider the effect of pos itive values of the convenience yield was conducted.

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81 CHAPTER 5 RESULTS AND DISCUSSION A Single Product Stand and the Geometric Brownian Motion Price Process In the following section the entire merchantable output of the stand at any rotation age was treated as a single undifferentiated product, in this case pulpwood. As argued earlier, this would be the case for a stand expe riencing low relative timber product prices. Figure 5-1 plots the per acre merchantable timber yield curve for a cutover slash pine stand in Florida with the following si te description and management history 0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.002 9 16 23 30 37 44 51 58 65 72 79 86 93 10Rotation age (Years)Merchantable Yield (Tons/Acre) Total Merchantable Wood Figure 5-1. Total per acre merchantab le yield curve for slash pine stand

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82 Site Index -60 (age 25) Site PreparationBurning only Initial planting density800 Trees per acre Thinnings – None Parameter values used for the anal ysis are listed in Table 5-1. Table 5-1. Parameter values used in analysis of harvest decision for single product stand with GBM price process _________________________________________________________________________ Parameter Effective Date/ Unit Value Period _________________________________________________________________________ FOB price II Qtr 2005 $/Ton 21.96 Stumpage price II Qtr 2005 $/Ton 7.42 Harvesting and transportation cost II Qtr 2005 $/Ton 14.54 Initial plantation expenses II Qtr 2005 $/acre 330.00 Estimated land rent II Qtr 2005 $/acre/year 34.00 Other annual expenses II Qtr 2005 $/acre/year 10.00 Estimated standard deviatio n 07-01-2005 Annual 0.29 of GBM price process Risk free rate 07-01-2005 %/annum 3.64 Constant convenience yield Annual 0.00 Present age of stand 07-01-2005 Years 20.00 ____________________________________________________________________ In order to value the option to postpone the clear-cut harvest the FOB price for pulpwood was modeled as a stochastic variable following a GBM process with a constant standard deviation of 0.29. A binomial latt ice was constructed usi ng Equations 3-28, 3-29 and 3-37 for this stochastic variable with a one year period. The backward recursive option pricing procedure was then implemented to determine the option value. The GAUSS Light version 5.0 (Aptech Systems, Inc.) software was used for finding solutions. The per acre pre-tax value of an immediate harvest an d sale as pulpwood of the entire merchantable yield at rotation age 20 at current stumpage price of $7.42/ton was

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83 $567. The maximum or terminal rotation age considered was 43 years. At $966/acre or $12.64/ton the calculated option value was higher than the value of immediate harvest. Figure 5-2 plots the upper bounds of the st umpage prices for the harvest region or the crossover price line. Since a discrete time approximation with large period values (annual) was used continuity wa s sacrificed i.e., the reported values of crossover prices display large jumps. The crossover line has also been smoothened to remove the incongruities in the data recovered from the discre te lattice structure. 0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00 160.00 180.00 200.00 2021222324252627282930313233343536373839404142 Rotation Age (Years)Stumpage Price ($/Ton) Optimal cross-over prices Figure 5-2. Crossover price line for sing le product stand with GBM price process The region to the RHS of the line is th e harvesting region and to its LHS is the continuity region. The form of the crossover line suggests that the optimal strategy will comprise of harvesting only if the rotati on age approaches the terminal age and the stumpage prices decrease to zero. As th e rotation age approaches the terminal age harvesting at higher stumpage prices becomes feasible. These results conform to the

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84 findings of Thomson (1992a) and the discussi on in Plantinga (1998) on the results of a Geometric Brownian motion price process. Th e form of the optimal harvesting strategy implies that harvesting was only feasible to avoid uneconomic outcomes or when there was a low probability of improving returns by waiting any further in the time left to the terminal date. This result for the GBM price process is al so confirmed from the results for plain financial (American) options on non-dividend bearing stocks that are always optimally held to the maturity/terminal date. The cro ssover line for stand harvesting is observed because of the presence of the intermediate expenses. Sensitivity Analysis Sensitivity of the results to changes in values of various parameters was considered next. The land rent was re-estimated to reflec t the change in value of the parameter under consideration. First, the respon se of the results to changes in intermediate expenses was considered. The option value corresponding to an increase in intermediate expenses by $10/annum/acre was $859/acre, a decrease of mo re than $100/acre. On the other hand the option value for an increase in intermedia te expenses by $40/annum/acre decreased the option value to $617/acre a drop of about $350/acre. If the higher intermediate expenses are considered to arise from payments for purchase of insurance against non-marketed un desirable risks, it is possible to see the effect that catastrophic risks have on the ha rvesting strategy and option values. Thus, the observed empirical rotations of less than 30 years could be partly explained by the presence of non-marketed undesirable risks. On the other hand, lowering intermediate expenses by $10/annum/acre increased the option value to $1,088/acre, an increase of more than $100/acre. Thus, positive and

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85 previsible cash flows in the form of, say, non-timber incomes or aesthetic values would lead to longer optimal rotations. 0 50 100 150 200 250 300 350 400 2021222324252627282930313233343536373839404142 Rotation age (Years)Stumpage Price ($/Ton) Unchanged Intermediate Expenses Less $5 Less $10 Less $20 Figure 5-3. Crossover price lines for different levels of intermediate expenses Figure 5-3 illustrates the effect of ch anging the magnitude of intermediate cash flows. As the intermediate cash flows in th e form of expenses or negative cash flows increase the crossover line shifts to the left towards lower rotation ages From the option pricing theory it is known that option value is directly related to the magnitude of the variance of the underlyi ng stochastic asset value. The variance for stumpage prices estimated from the TMS data may be higher than the variance experienced by individual pulpwood stand ow ners for reasons discussed earlier. Higher variances mean the possibility of higher pos itive payoffs even while the effect of the higher negative values is limited to zero.

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86 The results of the sensitivity analysis for different levels of variances confirmed the known behavior of option values. The option va lue for a standard deviation value of 0.20 was $765/acre as compared to $966/acre for the base standard deviation of 0.29. The option value dropped further to $658/acr e for a standard deviation of 0.10. Figure 5-4 shows that when the variance leve l is lower the crossover lines lie to the left of higher variance models so that optimal harvesting at lower rotation ages as well as lower stumpage prices becomes feasible. This im plies that in a situation of large expected variances arising, say, from an unpredictable regulatory environment, harvesting should be optimally postponed. 0 20 40 60 80 100 120 140 160 180 20020 22 24 26 28 30 32 34 36 38 40 42Rotation age (Years)Stumpage Prices ($/Ton) Unchanged Standard Deviation 0.29 Standard Deviation 0.2 Standard Deviation 0.1 Figure 5-4. Crossover price line for di fferent levels of standard deviation Instead of using a convenience yield value of zero, the use of a positive constant convenience yield will alter the risk-neutral expected drift of the process (Equation 3-38). For a constant convenience yield of 0.005 the option value dropp ed to $874/acre, dropping further to $803/acre for convenien ce yield value 0.01 and to $755/acre for

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87 convenience yield value 0.015. Since higher levels of convenience yield are associated with low levels of inventory and associated hi gher market prices, it suggests that optimal rotations should be shorter when the markets are tight. Figure 5-5 plots the effect of different le vels of constant convenience yield on the crossover price lines. It show s the leftward shift of the crossover lines in response to higher levels of constant positive convenience yields. 0 20 40 60 80 100 120 140 160 180 200 2021222324252627282930313233343536373839404142 Rotation age (Years)Stumpage Price ($/Ton) Convenience Yield =0 Convenience Yield = 0.005 Convenience Yield = 0.01 Convenience Yield = 0.015 Figure 5-5. Crossover price lines for vary ing levels of positiv e constant convenience yield It iz also of interest to know if the opt imal decision changes for a different current price i.e., does a higher or lower current stumpage price induce earlier harvesting. The per acre option value corresponding to a pres ent stumpage price of $1/ton ($76/acre) was $7/ton ($548/acre). On the other hand the pe r acre option value for a present stumpage price of $20/ton ($1,529/acre) was $25/ton ($1,933/acre). The po ssibility of higher

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88 payoffs as a result of higher current prices inflates the land rent reducing the relative option values. Figure 5-6 plots the results of consideri ng different levels of current stumpage prices. The plots show that the cross-over price lines shift to the left for a price increase and vice versa. This results pa rtly from the effect of a di rect relation between land rent and current prices. All other things being cons tant, a higher current timber price increases the present land value which increases the cost of waiting through the land rent. At the same time higher present stumpage price al so means lower possibility of unfavorable outcomes but this effect is overwhelmed by the increase in land values. 0 20 40 60 80 100 120 140 160 180 2002 0 22 24 2 6 28 3 0 3 2 34 3 6 38 40 4 2Rotation Age (Years)Stumpage Price ($/Ton) Unchanged Stumpage Price = $7.42/Ton Stumpage Price = $1.00/Ton Stumpage Price = $20/Ton Figure 5-6. Crossover price lines for different levels of current stumpage price

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89 Next, by changing the present rotation age of the stand from 20 to 25 and 30 we can observe the drop in option values commonl y associated with financial options as the time remaining till the terminal date is reduced This is due to the lower probability of higher payoffs in the remaining time. For the timber stand, for a present rotation age of 25 the associated option value was $9.97/ton ($999/acre). Similarly, for a present rotation age of 30 the option value was $8.88/ton ($1,027/acre). Finally, the effect of a change in the initial planting density was studied. The current plantation establishment expenses were adjusted to reflect the cost of planting less plants which effects the estimated land rent, though only marginally. The important observation is that lower initial planting de nsities did not change option values which were $12.62/ton ($909/acre) for 700 tpa an d $12.64/ton ($781/acre) for 500 tpa. The option value calculated earlier for a 800 tpa initial planting dens ity was $12.64/ton. Comparison with the Dynamic Programming Approach This section applies the DP approach to the single product slash pine pulpwood stand with timber prices following a GBM pro cess. The binomial lattice was set up using Equations 3-28 and 3-29 with the subjective probability given by Equation 3-27 instead of the risk-neutral probabili ty given by Equation 3-37. The estimated value of the drift for pulpwood stumpage prices was 0.05 (with a standard error of n =0.053 or >100%). A variety of discount rates have been used in published forestry literature using the DP approach, the most common being a real rate of 5%. Since this analysis was conducted in nominal terms and the average inflation estima ted from the PPI series was 3%, a nominal discount rate of 8% was used in this DP analysis. Further, some of the published literature assumes the intermediate costs are constant in real terms. Therefore, for

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90 comparability, future values of intermediate expenses including the estimated land rent and harvesting and transportation cost were in flated at the average inflation rate of 3% computed from the PPI series. The land rent was estimated using the DP procedure. All other parameter values used were unchanged from Table 5-1. The option value derived from the DP approach, parameterized as above, was $2,393/acre. This value was more than tw ice the option value derived using the CC approach i.e., $966/acre. The use of a discount rate of approximately 12.5% brought down the estimated option value using the DP approach close to the option value estimated using the CC approach. As noted and illustrated in Hull (2003) the appropriate discount rate for options is much higher than the discount rate applicable to the underlying asset. First, it should be noted that at 12.5% th e discount rate is much higher than typical rates considered in forestry l iterature on options an alysis. Second, this discount rate is not a constant but would vary according to the parameter values of the problem. This is evident from the sensitivity of the option values to parameters exhibited above. This illustration serves to highlight the problems associated with using the DP approach in the absence of a method for determining the appropriate discount rate. A Single Product Stand and the Mean Reverting Price Process In this section the optima l harvesting strategy for th e single product (pulpwood) stand is analyzed with a mean reverting FO B price process. The stand description and management history were identical to th ose considered for the GBM price process analysis. The parameters used in the ba sic analysis are listed in Table5-2.

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91 The problem was modeled by consideri ng only the FOB price for pulpwood as stochastic following a MR process of the fo rm given by Equation 4-22 with a constant standard deviation of 0.22 and constant reversion coefficient with value 0.18. Table 5-2. Parameter values used in analysis of harvest decision for single product stand with MR price process _________________________________________________________________________ Parameter Effective Date/ Unit Value Period _________________________________________________________________________ FOB price II Qtr 2005 $/ton 21.96 Stumpage price II Qtr 2005 $/ton 7.42 Harvesting and transportation cost II Qtr 2005 $/ton 14.54 Mean FOB price level II Qtr 2005 $/ton 24.44 Initial plantation expenses II Qtr 2005 $/acre 330.00 Estimated land rent II Qtr 2005 $/acre/year 24.00 Other annual expenses II Qtr 2005 $/acre/year 10.00 Estimated standard deviatio n 07-01-2005 Annual 0.22 of MR price process Estimated constant reversi on 07-01-2005 Annual 0.18 coefficient Risk free rate 07-01-2005 %/annum 3.64 Constant convenience yield Annual 0.00 Present age of stand 07-01-2005 Years 20.00 Estimated average inflation rate %/annum 3.00 ____________________________________________________________________ The option value at $1,290 was higher than present stumpage value of $567. From the form of the crossover price lin e (drawn after smoothing) for the mean reverting FOB prices (Figure 5-7) it is evident that the strategy for the optimal harvest is significantly different than that for the GB M prices. The form of the crossover line suggests that the optimal strategy would be to harvest if a sufficiently high stumpage was received at each rotation age, the crossover price declining with the rotation age. These results are consistent with those reported for the reservation prices obtained using search algorithms and for other studies with first or der autoregressive or mean reverting prices.

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92 0 2 4 6 8 10 12 14 16 18 20 2021222324252627282930313233343536373839404142 Rotation Age (Years)Stumpage Prices ($/Ton) Crossover Price line Figure 5-7. Crossover price line for sing le product stand with MR price process The form of the crossover price line for the MR price process is a result of the characteristics of the process. By definiti on the MR process has a higher probability of moving in the direction of the mean value in the following period. Therefore, when the present stumpage price is higher than the mean the probability is higher that the price will fall in the following period and vice versa. This implies the optimal strategy suggested by the form of the crossover line, i.e., to harvest if the stumpage price is high as it is more likely to fall if the option to wait is chosen The crossover line is downward sloping as it approaches the terminal period since the po ssibility of profiting from waiting is lower and immediate harvest at lower prices is justifie d. The dropping rate of yield increase of the stand also influences the shape similarly.

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93 The Multiple Product Stand and Geomet ric Brownian Motion Price Processes Allowing for the presence of multiple prod ucts and assuming that their individual prices follow the GBM process with correla tion between the two price processes should reflect the empirical problem better than th e single product stand case. The case of two products i.e., pulpwood and ch ip-n-saw were considered.. The yield curves for the two timber pro ducts are plotted in Figure 5-8. The parameters used for the analysis are listed in Table 5-3. 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.002 7 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82Rotation Age (Years)Merchantable Yield (Ton/acre) Pulp wood yield CNS Yield Figure 5-8. Merchantable yiel d curves for pulpwood and CNS The present stumpage price value for the CNS product was selected so that it was closer to the present stumpage for pulpwood than the price reported by TMS, to reflect the low relative price phenomena. The vari ance parameter for CNS was calculated from

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94 TMS data as illustrated above for pulpwood. The CNS stumpage variance was imputed to the CNS FOB variance while a common harves ting and transportation cost was used. Table 5-3. Parameter values used in analys is of harvest decision for multiproduct stand with GBM price processes _________________________________________________________________________ Parameter Effective Date/ Unit Value Period _________________________________________________________________________ Pulpwood FOB price II Qtr 2005 $/ton 21.96 Stumpage price II Qtr 2005 $/ton 7.42 Harvesting and transportation cost II Qtr 2005 $/ton 14.54 Estimated standard deviatio n 07-01-2005 Annual 0.29 of GBM price process C-n-S FOB price II Qtr 2005 $/ton 26.54 Stumpage price II Qtr 2005 $/ton 12.00 Harvesting and transportation cost II Qtr 2005 $/ton 14.54 Estimated standard deviatio n 07-01-2005 Annual 0.24 of GBM price process Initial plantation expenses II Qtr 2005 $/acre 330.00 Estimated land rent II Qtr 2005 $/acre/year 40.00 Other annual expenses II Qtr 2005 $/acre/year 10.00 Risk free rate 07-01-2005 %/annum 3.64 Constant convenience yield Annual 0.00 Present age of stand 07-01-2005 Years 20.00 ____________________________________________________________________ The model allowed the decision maker to sell differentiated products as long as the stumpage price for CNS was greater than the pulpwood stumpage price. But if the CNS price fell below the pulpwood price, the st and owner could sell the entire output as pulpwood. The option price was $1,325 as compared to the present market value of $585. The crossover lines (after smoothing) for rotation ages 34 to 39 are plotted in Figure 5-9. Compared to the single product stand analysis harvesting is optimal at higher

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95 rotation ages when the second timber produc t is introduced, even though the relative prices considered were half the TMS reported average. The optimal strategy suggested is the same i.e., to harvest when the stumpage prices are sufficiently low and to hold otherwise. Crossover Stumpage Price lines by Rotation ages for Pulpwood and CNS0 10 20 30 40 50 60 70 80 0102030405060708090100110120130140 CNS Stumpage Price ($/Ton)Pulpwood Stumpage Price ($/Ton) Rotation age 34 35 36 37 38 39 Figure 5-9. Crossover price lines for multiproduct stand The results obtained for the multiproduct stand with correlated price processes are due to several factors. First, the yield of th e late rotation product wh ich is higher valued, increases in quantity at a high rate as the rotation increases in length. This increases the size of the net harvest revenues per period. Second, the presence of positive autocorre lation between the two price processes means that their joint variance is higher th an the variance of th e single product stand. This can be interpreted from Equation 3-43 the terms in the first box bracket. The positive correlation between variance and op tion values has already been shown above.

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96 Finally, the stand owner has an additional option to sell the crop at the price of the lower diameter product should the price of the higher diameter product fall below it. This option increases future payoffs and adds further value to the stand. Thinning the Single Product Stand and the Geometric Brownian Motion Price Process In order to analyze the commercial thinning option for the single product (pulpwood) stand with a GBM price proce ss, the following assumptions were made i. A single commercial thinning in the form of a row thinning is planned involving the removal of every third row of trees from the stand. No selection is involved. It is assumed that this one in three row thinning yields one third of the existing merchantable yield in the stand as harvest. ii. The residual stand continues to grow fo r the balance of the rotation and yields merchantable timber which equals the unthinned stand yield less the quantity removed at thinning. That is, the total me rchantable yield of the stand from thinning plus clear-cut harvest equals the merchantable yield from clear-cut harvest of the unthinned stand iii. The stumpage price for thinnings is half that for a clear-cut harvest at any time on account of the higher per unit harvesting and transportation cost. The failure to obtain significant improvem ent in total yield from a late rotation thinning was modeled on the empirical observations of Johnson (1961) discussed above. Figure 5-10 depicts the merchantable yiel d curves after the a pplication of a single thinning at different rotation ages. The para meters used in the analysis were unchanged from Table. 5.1 including the estimated land rent. The following three options were available to the decision maker at each age 1. Clear-cut harvest now. 2. Postpone clear-cut harvest without thinning. 3. Postpone clear-cut harvest after thinning. The decision maker chooses amongst the th ree options on the ba sis of the highest present value. The results obtained were iden tical to the case of the single product stand

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97 with GBM price process without the thinning option, indicating that thinning was not optimal at any age and the thinning option adds no value. This case serves to show that the presence of an option does not always add value to the asset. In this particular case the value of flexibility was not sufficient to make up for the loss of revenues on account of the lower thinning stumpage price. 0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.002 5 8 1 1 14 1 7 20 2 3 2 6 2 9 3 2 3 5 3 8 41 4 4 47 5 0 53 5 6 59Rotation Age (Years)Merchantable Yield (Tons/Acre) Unthinned Stand Thinning age 20 Thinning age 25 Figure 5-10. Single product stand merchantable yield curves with single thinning at different ages If the structure of the slash pine standÂ’s response to late thinning was maintained the results did not change for higher/lower thinning intensities. This further confirmed that commercial thinning added no value to the investment and the option could be disregarded by stand owners. The sensitivity of the results to higher levels of thinning stumpage prices was also analyzed. The result remains unchanged for higher thinning stumpage prices as long as

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98 they remain lower than the clea r-cut harvest stumpage price. (up to nine tenths the clearcut harvest stumpage was used). Discussion In all cases considered, the option value calculated was higher than the present stumpage value of the stand. This means that the optimal decision was to postpone harvest for a later date, i.e., the option should be retained and immediate harvest was not optimal policy. But do the results imply that the decision to postpone harvest was optimal for all decision makers? To answer this question it is necessary to look at the nature of results obtained through options analysis. An insight into the re sults of the options analysis can be gained by treating investment decision making unde r risk as gambling. The no-arbitrage condition that underlies the option pricing method essentially means finding a price for the gamble that makes it fair. A fair gamble is one that does not favor either party to the gamble. This means that the expected value to either party from the gamble is zero. In a competitive market all risky assets/investment opportunities/gambles would be fairly priced. Ross (2002) uses the gambling analogy to introduce the arbitrage pricing theorem which states that either there exists a probabi lity vector on the set of possible outcomes of an experiment under which the expected return of each possible wager on the outcomes is equal to zero or there exists a betting strategy that yields a positive win for each outcome of the experiment. The probabilities that result in all bets being fair are called risk neutral probabilities. The option pricing method uses risk neutral probabilities to produce an arbitrage free price for an asset. When this arbitrage free price is compared to the current market value, a decision can be made to hol d or dispose off the asset. If the current market value is higher than the arbitrage free price, it means that a profit can be earned by

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99 cashing in the market value. On the other hand if the asset market value is lower than the arbitrage free price the gamble on the asset is under priced. This is because if one decides to hold the asset one exchanges a lower market value asset for a higher valued asset. If the decision to accept the gamble and hold the asset is taken, option pricing does not guarantee any profits. The option pricing theory only asserts that the gamble is favorably priced or in other words a gamble with similar risk can be only had in the competitive market for a higher price. But, is it necessary that the asset holder should be inclined to take even this favorably priced gamble? It could be argue d that an individual that chose to invest in the asset in the first place and thus take on the risks associated with the asset would be likely to take this fa vorable gamble. But, the actual decision would depend on the individualÂ’s utility function at the time of making the decision along with other factors like the size of wealth at stake. Some empiri cal studies (Dennis1989, 1990 and Jamnick and Beckett 1987) of NIPF ha rvesting behavior have used econometric models to determine that stand owner characte ristics like age & wealth effect the harvest decision. Practical decision making would also be gu ided by factors like taxes, the ability to avail of government subsidies fo r regeneration, the ability to borrow, etc. Fina et al. (2001) study the effect of debt principa l repayment obligation for NIPF landowners on harvest scheduling. The paper quotes Birch (1996) to argue that NIPF landowners may be less endowed financially and dependent on the credit market for carrying out their operations. The paper demonstrates that rese rvation prices are a pos itive function of the time to debt principal payments, i.e., immine nt repayment of debt obligations leads to timber land owners accepting lower reservation prices.

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100 Moreover, before any conclusions regardin g optimal decisions are drawn from the analysis for empirical use it must be considered that the analysis itself is incomplete because it does not treat the non-marketed risks. The exclusion of non-marketed risks from the analysis does not lessen its im portance to the decision making process. Catastrophic risks like damage from ex treme weather conditions are capable of destroying most of the value of a stand. Stan d investors would be reluctant to retain such risks and would be willing to pay to transfer them to the market place through the use of market instruments like insurance products we re they available. When this payment is added to the cost of holding, the waiting decision becomes less appealing and the option value decreases. This has been shown through th e analysis of sensitivity of the results to increased intermediate expenses. The failure to provide a comprehensive tr eatment for non-marketed risks together with market price risk is common to all options analysis research in forestry, unrelated to the CC approach. As shown earlier, in the abse nce of information on the market cost of insurance against catastrophic risk the CC analysis is incomplete and the option values derived are on the higher side. But when a market for these risks does not exist other methods have been suggested to factor th e non-marketed risks into the analysis. Luenberger (1998) and Smith and Nau (1995) de scribe an integrated approach to treating the public (marketed) and private (non-marketed) risks together in the analysis. The approach involves using subjective probabilit ies for the private risks and risk-neutral probabilities for the marketed risks. There are other reasons to discount the re sults of the analysis. The assumptions underlying the CC analysis are cr itical to the empirical validity of the prescriptions. For

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101 example the absence of a common rate for borrowing and lending can effect the analysis. Commonly, the borrowing rate available to i ndividuals is signifi cantly higher than the lending rate. The effect of the adverse spread is to reduce the fair value of the option to the purchaser. Hughes (2000) incorporates this adverse spread into the real option valuation of the assets of the New Zealand Forest Corporation and the study provides an illustration of this effect. Tahvonen et al. (2001) studies the Faustmann problem under conditions of borrowing constraints and conc ludes that borrowing cons traints in the form of high borrowing costs l ead to shorter rotations. Next, the timber markets are characteri zed by an absence of freely available information. Haight and Holmes (1991) point ou t that using the reserv ation price policies for harvesting requires the monitoring of stum page prices and readiness to complete a sale contract. Assuming that the cost of information is of th e same magnitude as the cost of timber cruising, the study found that as the fixed costs increased harvesting is acceptable at lower prices (hence earlier) to avoid paying the additional costs of price monitoring. The study concludes that a fixed ro tation harvest may be preferred as it does not require stumpage price monitoring. The same cost of information can be readily applied to yield determin ation, input costs etc. Timber markets also operate with subs tantial friction or transaction costs. Washburn and Binkley (1990a) observe that th e weak-form efficiency test implicitly assumes that movement of stumpage in and out of storage is frictionless; that is, that timber sale plans can be instantaneously adju sted at no cost. But they question this assumption, arguing that the time and cost involved in consummating a timber sale might produce short-run friction in the stumpage market. In this condition, the failure of

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102 stumpage prices to adjust instantaneously to new information might i ndicate that the costs of adjustment exceed the possible economic gains. Similarly, the failure to empirically observ e the other critical assumptions regarding continuous trading, short sales, etc., also undermine the results of the analysis. Empirically, values of parameters like vari ance, risk-free rates (term structure) and convenience yield evolve with time. Some pa rameter values used for the real options analysis are previsible like the risk-free ra te or the convenience yield (where a futures market of sufficient term exists). Others li ke the variance have to be estimated from historical data. Historical volatility is an impe rfect substitute for expe cted volatility. It is known from the operation of financial markets that the volatility implicit in actual market trades in options is seldom accurately predicte d by historical data. It is the perception of future volatility that determines optio n values rather than its history. Another important observation is that there are no fixed reservation prices that can be predetermined and used for decision making by stand owners. The crossover prices derived from the analysis are not fixed a nd are sensitive to evolving market based parameter values like the variance, convenie nce yield and the present timber price (and hence the estimated land rent) as shown above. The form or shape of the crossover price lines does suggest the nature of the optimal strategy but even that is not useful information unless the debate over the nature of stochasticity of timber prices could be settled. As illustrated earlier, the optimal stra tegy suggested by the form of the crossover price line for the two popular st ochastic processes for timber price considered in this analysis is diametrically opposite.

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103 The one key conclusion from the analysis is the importance of market information to the optimal stand management and invest ment decision making processes in the presence of uncertainty. It is widely ackno wledged that timber markets operate with a woeful lack of information, with the stand ow ners being the lowest in the hierarchy of the informed. This inadequacy is manifest in the importance of consultants for the stand sale process and the significant transaction costs imposed thereby. It is common for stand owners to be unaware of the market price of their product. Determining a useful estimate for the unobserved land rent is still an unreso lved issue. In the absence of futures markets the determination of convenience yields remains a problem. The absence of markets for trading in presently non-marketed risks m eans that their market values are unknown. Without access to critical information required for investment decision making the significant value of flexibility in management of stands cannot be measured. Inability to measure the option value means that stand owne rs cannot fully realize this value which in turn implies that investment decision ma king for timber stand is not optimal. Recently, some timber purchasers have in troduced innovation to the marketplace by using the internet to disseminate inform ation about their purchasing practices and provide a transparent auction site for the selle rs to bid their selling prices. Perhaps the greatest gain of this innovation will come in the form of the flow of information to the stand investors. Greater transparency should redress seller concerns about being on the losing side of the transaction and enco urage informed decision making resulting in increased efficiency. With the increasing presence of corporate timberland managers like TIMOÂ’s ( Timber Investment Management Organizatio ns) and REITÂ’s (Real Estate Investment

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104 Trusts), it is conceivable that the demand fo r financial innovations will increase and will result in the creation of markets for trading in presently non-marketed risks. The same developments should spur the creation of fo rward/futures markets. These markets will create tremendous information for the decisi on makers and should increase efficiencies all around. Recommendations for Further Research A vast number of studies have researched timber stand investment decisions with a similarly large number of approaches. Howe ver, the treatment of decision making under risk from the perspective of an investment analysis is a relatively new subject with a number of inadequately researched asp ects. Some areas recommended for further research are i. The simultaneous production of multiple tim ber products in sta nds effects decision making in ways that are inadequately unders tood at present. Research on modeling the complexities of dealing with multiple prod ucts in conjunction with commercial thinning or flexible silvicultural investme nt options could provide insight into risk management at the stand le vel amongst other things. ii. Non-marketed sources of risk are ignored or insufficiently treated in the analysis of investment decision making. Application of some of the available models for dealing with non-marketed risk can improve the va lue of results obtained from investment analysis. iii. The role of stochastic land rent or the su rplus assigned to land in the real options analysis has never been researched. Different mathematical methods have been used by researchers to determine land value but the equivalence of these methods remains to be shown.

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105 APPENDIX CORRELATION OF FIRST DIFFERENCES OF AVERAGES OF TWO RANDOM CHAINS The correlation of two period averaged random chains can be calculated by extending Working (1960). Following Working (1960) let the two random chains be represented by 1 iiiXX ()0,var()1,(,)0iiiijEcor for 0 j 1 iiiYY ()0,var()1,(,)0iiiijEcor for 0j Then, from Working (1960) 2 ()()212 ()() 3xy imimmm VarVar m as mgets larger Here mis the number of evenly spaced cons ecutive time series data averaged and ()111111 (.....)(.....)x imiiimimimiXXXXXX mm is the difference of consecutive averages. The instantaneous correlation to be estimated is ()() ()() ()()(,) (,) ()()xy imim xy imim xy imimCov Cor VarVar ()()(,) 22 33xy imimCov mm ()()(,) 2 3xy imimCov m Or in the general case where 2()ixVar and 2()iyVar

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106 ()() ()() 22(,) (,) 2 3xy imim xy imim xyCov Cor m To solve for()()(,)xy imimCov let(,)iiCovd From the Markov property of i X and iY and assumptions regardi ng the random nature of i and i it follows that (,)0iijCov for 0 j We can express ()121111 (1)(2).....(1).....x imiiimiiimmmmm m ()121111 (1)(2).....(1).....y imiiimiiimmmmm m Therefore, 22 ()()112211 21 (,)(1)(,)(2)(,).......(,)xy imimiiiiimimCovmCovmCovCov m 2222 2(1)(2)...1(1)....1 d mmmm m 221 3 m d m 2 3 md as mgets larger So, ()() ()() 22(,) (,) 2 3xy imim xy imim xyCov Cor m 222 3 2 3xymd m 22 xyd Therefore, the correlation of the first differences of the two averaged random chains would equal the correlation of th e unaveraged random chains and requires no correction.

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107 LIST OF REFERENCES ADAMS, D. 2002. Harvest, Inventory, and Stumpage Prices: Consumption Outpaces Harvest, Prices Rise Slowly. J ournal of Forestry. 100(2):26-31. AF&PA (AMERICAN FOREST AND PAPER ASSOCIATION). 2003. Florida : Forest and Paper Industry at a Glance. 2 p. Retrieved 03-15-2006 from www.afandpa.org ARONSSON, T., AND O. CARLEN.2000. The Determinants of Forest Land Prices. An Empirical Analysis. Canadian Journa l of Forest Research. 30(4):589-595. BARNETT, J.P. AND R.M. SHEFFIELD.2004. Slash Pine: Characteri stics, History, Status, and Trends. In Dickens, E.D.; Barnett, J.P.; Hubbard, W.G.; Jokela, E.J., eds. 2004. Slash Pine: Still Growing and Growing! Pr oceedings of the Slash Pine Symposium. Gen. Tech. Rep. SRS-76. Asheville, NC: U. S. Department of Agriculture, Forest Service, Southern Research Station. 1-6. BAXTER, M. AND A. RENNIE.1996.Financial Calculus: An Introduction to Derivative Pricing. Cambridge University Press. New York, NY.233 p BENTLEY, J.W., T.G. JOHNSON, AND E. FORD.2002. Florida’s Timber Industry—An Assessment of Timber Product Output a nd Use, 1999. Resource Bulletin. SRS–77. Asheville, NC: U.S. Department of Agricu lture, Forest Service, Southern Research Station.37 p. BENTLEY, W.R., AND D.E. TEEGUARDEN.1965.Financial Maturity: A Theoretical Review. Forest Science. 11(1):76-87. BIRCH, T.W.1996. Private Forest Land Owners of the United States, 1994. USDA Forest Service, Northeastern Forest Experiment Station Research Bulletin NE-134.183 p. BIRCH, T.W. 1997. Private Forest-Land Owners of the Southern United States, 1994. Northeastern Forest Experiment Stat ion. Research Bulletin NE-138. 147 p. BLACK, F., AND M. SCHOLES.1973. The Pricing of Options and Corporate Liabilities. Journal of Political Economy. 81 (3):637-654. BRAZEE, R.J., AND R. MENDELSOHN.1988.Timber Harvesting w ith Fluctuating Prices. Forest Science.34:359-372. BRAZEE, R.J., AND E. BULTE.2000. Optimal Harvesting and Thinning with Stochastic Prices. Forest Science. 46(1):23-31.

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108 BRENNAN, M.J. 1991. The Price of Convenience and the Valuation of Commodity Contingent Claims. In D. Lund and B. Oksendal (eds) Stochastic Models and Option Values (Contribution to Economic Analysis # 200 ). North-Holland. Amsterdam, The Netherlands. 33-71. BRIGHT, G., AND C. PRICE. 2000. Valuing Forest Land unde r Hazards to Crop Survival. Forestry. 73(4):361-370. BROCK, W.A., M. ROTHSCHILD, AND J.E. STIGLITZ. 1982. Stochastic Capital Theory. in: G. Feiwel, ed..Joan Robinson and Modern Economic Theory. Macmillan. London. 591-622. BROCKWELL, P.J., AND R.A. DAVIS. 2003. Introduction to Time Series and Forecasting. 2nd Edition. Springer-Verlag, New York, NY. 434 p. CARTER, D.R., AND E.J. JOKELA. 2002. FloridaÂ’s Renewable Fo rest Resources. School of Forest Resources and Conservation, Univer sity of Florida, Florida Cooperative Extension Service Document CIR14 33. 10 p. Retrieved 05-03-2006 from http://edis.ifas.ufl.edu/. CASTLE, E.N., AND I. HOCH. 1982. Farm Real Estate Price Components, 1920-78. American Journal of Agricult ural Economics. 64(1):8-18. CHANG, S.J. 1998. A Generalized Faustmann Model for the Determination of Optimal Harvest Age. Canadian Journal of Forest Research. 28(5):652-659. CLARKE, H.R., AND W.J. REED.1989. The Tree-Cutting Problem in a Stochastic Environment: The Case of Age Dependent Growth. Journal of Economic Dynamics and Control.13:569-595. CONSTANTINIDES, G.M. 1978. Market Risk Adjustment in Project Valuation. The Journal of Finance. 33(2):603-616. COPELAND, T.E., J.F. WESTON, AND K. SHASTRI.2004.Financial Theory and Corporate Policy. 4th Edition. Pearson Addison Wesley. New York, NY. 1000 p. COX, J.C., S.A. ROSS, AND M. RUBENSTEIN.1979. Option Pricing: A Simplified Approach. Journal of Financial Economics.7(3):229-263. DENNIS, D.F.1989. An Econometric Analysis of Harvest Behavior: Integrating Ownership and Forest Characteristic s. Forest Science. 35(4):1088-1104. DENNIS, D.F. 1990. A Probit Analysis of Harves t Decisions using Pooled Time-Series and Cross-Sectional Data. Journal of Environmental Economics and Management. 18(2):176-187.

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115 BIOGRAPHICAL SKETCH Shiv Nath Mehrotra was born in Vrindaba n, India, in 1965. After earning a Post Graduate Diploma in Forestry Management (P GDFM) from the Indian Institute of Forest Management, India, in 1991, he joined M/s. Century Pulp & Paper, India, in the Raw Materials department. In 1992 he earned a Ma ster of Arts (MA) degree in economics from Agra University, India. Serving M/s. Ce ntury Pulp & Paper in various capacities, he managed the fiber procurement operations and a farm forestry development program till June, 2002. In 2002 he was awarded a fellowshi p to pursue the docto ral program at the School of Forest Resources and Conservation, University of Florida. He joined the doctoral program in the fall of 2002, completing it in August, 2006.

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