A simultaneous equation model of futures market trading activity

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Title:
A simultaneous equation model of futures market trading activity
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Futures market trading activity
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xiii, 262 leaves : ill. ; 28 cm.
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Behr, Robert M., 1954-
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Commodity exchanges -- Mathematical models   ( lcsh )
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Thesis:
Thesis (Ph. D.)--University of Florida, 1981.
Bibliography:
Includes bibliographical references (leaves 258-261).
Statement of Responsibility:
by Robert M. Behr.
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Typescript.
General Note:
Vita.

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A SIMULTANEOUS EQUATION MODEL
OF FUTURES MARKET TRADING ACTIVITY










BY

ROBERT M. BEHR


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


UNIVERSITY OF FLORIDA


1981


































This research is dedicated to my wife, Sarah, who has provided en-

couragement and many hours of hard labor towards its completion. With-

out Sarah it is doubtful whether this dissertation would have been

written. I will forever be indebted for the compassion and understanding

that she has given over the past three years. I would also like to

dedicate this work to my parents, who never gave up hope.















ACKNOWLEDGMENTS


The author would like to thank Dr. Ron Ward for his assistance in

this research as well as the guidance received during the course of

his graduate studies. Aside from the faculty advisory role, Dr. Ward

has been a close personal ally which went unappreciated too often during

the grueling Ph.D. ordeal. Thanks also go out to Dr. Leo Polopolus, Dr.

Richard Kilmer, Dr. Jim McClave, Dr. W.W. McPherson and Dr. Jim Simpson,

each of whom were very supportive as committee members. Special thanks

are accorded to Ramona Rochester, Susan Howard, and Sarah Behr for their

thankless efforts in putting this manuscript together.










TABLE OF CONTENTS

Page

ACKNOWLEDGMENTS . . iii

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

LIST OF FIGURES . . ... ...... .ix

ABSTRACT. . . ... .... xi

CHAPTER

1 INTRODUCTION . .... ... 1

The Analogy with Money. . 2
Recent Trends . . 4
Economic Functions of Futures Markets 8
Economic Problems and Concerns. ... 12
Statement of Objectives . .... 17
Methodology . . .17
Overview. . .... 19

2 LITERATURE REVIEW. . ... 20

Overview of Futures Market Literature .. 20
Telser's Model. . ... 31

3 THEORETICAL FUTURES BENEFIT MODEL. ... 38

The Benefit Equation. . ... 38
Exogenous Effects on Trading. . ... 46
The Exogenous Influences. . .. 50
Summary . . ... 64

4 A MATHEMATICAL FUTURES TRADING MODEL . 66

Variable Definitions. . ... 67
Choice of Functional Form . .... .93
Mathematical Trading Model . ... 100
Endogenous Variable Interrelationships. .... 103
Summary . . 106

5 AN ECONOMETRIC ANALYSIS OF FUTURES TRADING .... 108

The Econometric Model . .. 108
Empirical Futures Benefit Model ... 122
Summary . . 137











CHAPTER


6 POLICY IMPLICATIONS AND TRADING RESPONSES


The Partial Effects .
The Total Effects .
Speculative Index .
Summary . .


7 SUMMARY AND CONCLUSIONS .


Futures Trading Overview. .
Futures Market Conclusions.
Importance of the Research.
General Conclusions .


APPENDICES


A A LIST OF T
B DATA SOURCE
C THE DATA .
D THE CONCENT
E MODEL RESUL


HE MARKETS. .
S . .

RATED LIKELIHOOD FUNCTION .
TS . .


F THE ESTIMATION PROCEDURE . ...
G RELATIONSHIP BETWEEN NET BENEFIT FUNCTION AND MATHE-
MATICAL MODEL . . .


REFERENCES . .

BIOGRAPHICAL SKETCH .


S 138


Page










LIST OF TABLES


Table Page

1.1 Number of contracts traded. . 6

1.2 Average monthend open interest (in contracts) 7

1.3 Intertemporal movements in the coefficient of vari-
ation (CV) . . 9

1.4 Contract market designations: the agricultural
markets . .. .13

3.1 The hypothesized partial effects. ... 65

4.1 Classification of commodities by degree of perish-
ability . .. .87

4.2 Sign hypotheses of the r. .. ........ 102

5.1 First stage estimates of the logistic model using
Scheme 1 allocation . ... .123

5.2 Second stage estimates of the logistic model using
Scheme 1 allocation . ... .125

5.3 Value of the concentrated likelihood function over
the range of p (see equation 5.11). .. 129

5.4 Robustness of parameters of the hedging equation
with changes in p . .... .130

5.5 Third stage estimates of the logistic model using
Scheme 1 allocation (see equations 5.1, 5.2, and
5.3). . . .. ..... 133

5.6 Third stage estimates of the logistic model using
Scheme 2 allocation . .... .135

6.1 The model variables with univariate statistics. 140

6.2 The simulated partial effects of concentration. 142

6.3 Concentration ratios of selected industries 144

6.4 The simulated partial effects of the coefficient
of variation. . . 15T

6.5 Effect of price support policy on cotton futures
market . . 155












Reduced form parameters based on third stage esti-
mates using Scheme 1 (see equation 3.14) .

Total adjustment of the speculative index and
volume as age varies . .

A list of the markets used in the empirical
analysis . . .


A list of the variables used
analysis .

Commodity Time Series Data.

Commodity Time Series Data.

Commodity Time Series Data.

Commodity Time Series Data.

Commodity Time Series Data.

Commodity Time Series Data.

Commodity Time Series Data.

Commodity Time Series Data.

Commodity Time Series Data.

Commodity Time Series Data.

Commodity Time Series Data.

Commodity Time Series Data.

Commodity Time Series Data.

Commodity Time Series Data.

Commodity Time Series Data.

Commodity Time Series Data.

A list of the variables and


C.1

C.2

C.3

C.4

C.5

C.6

C.7

C.8

C.9

C.10

C.11

C.12

C.13

C.14

C.15

C.16

C.17


E.1


E.2


Page


180


191


209


Iin the empirical
. . 210

. . 215

. . 216

. . 217

. . 218

. . 219

. . 220

. . 221

. . 222

. . 223

. . 224

. . 225

. . 226

. . 227

. . 228

. . 229


their


corresponding


First stage estimates of the logistic model using
Scheme 1 allocation . .

Second stage estimates of the logistic model using
Scheme 1 allocation . .


231


238


239


Table

6.6


6.7


A.1


symbols


. . . P










Table Page

E.3 Third stage estimates of the logistic model using
Scheme 1 allocation (see equations 5.1, 5.2, and
5.3) . . 240

E.4 First stage estimates of the logistic model using
Scheme 2 allocation . . 241

E.5 Second stage estimates of the logistic model using
Scheme 2 allocation . . 242

E.6 Third stage estimates of the logistic model using
Scheme 2 allocation . .. .243

E.7 First stage estimates of the exponential model
using Scheme 1 allocation . .... .244
E.8 Second stage estimates of the exponential model
using Scheme 1 allocation. . 245

E.9 Third stage estimates of the exponential model
using Scheme 1 allocation . .... .246
E.10 First stage estimates of the exponential model
using Scheme 2 allocation . 247
E.11 Second stage estimates of the exponential model
using Scheme 2 allocation . .... .248

E.12 Third stage estimates of the exponential model
using Scheme 2 allocation . ... 249


viii










LIST OF FIGURES


Figure Page

3.1 The effect given a change in Z. . .. 49

3.2 The partia effect of an increase in basis risk
(BRO to BR ). . . 58

4.1 The effect of price variability on market activity. 82

4.2 The effect of age on hedging. . .. 85

4.3 Theoretical effect of government price supports on
hedging . . .. 88

4.4 Theoretical graph of the logistic function. ... 97

4.5 Theoretical effect of concentration on hedging. 99

5.1 Graph of the concentrated likelihood function for
the three structural equations (HDG, SPC, and VOL). 128

6.1 The simulated partial effects of concentration. 141

6.2 The simulated partial effects of firm numbers 146

6.3 The simulated partial effects of the correlation
coefficient (CC). . ... 148

6.4 The simulated partial effects of the coefficient of
variation (CV) . . ... 150

6.5 The simulated partial effects of the loan rate
variable (LN). ..... . 154

6.6 The simulated partial effects of age (AG) .... 158

6.7 The simulated partial effects of volume (V) 160

6.8 The simulated partial effects of the coefficient
of variation (CV) with respect to speculation 163

6.9 The simulated partial effects of the utility yield. 165

6.10 The simulated partial effects of volume (V) with
respect to speculation. . .. 167

6.11 The simulated partial effects of spreading (SV) .. 170










Figure Page

6.12 The simulated partial effects of the trend variable
(TT). . . .. ..... .172

6.13 The simulated partial effects of hedging (H*) .. 173

6.14 The simulated partial effects of exchange rank (ER) 175

6.15 Relationship between the total and partial effect
of concentration (CN) . ... 181

6.16 The relationship between total hedging open inter-
est and total supply. . ... 184

6.17 The total effect of concentration on hedging and
speculation, respectively .... .. 188

6.18 The effect of concentration on the speculative
index . .... .189

6.19 Plot of the speculative index and volume coordi-
nates derived from a simulation of market age 190















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


A SIMULTANEOUS EQUATION MODEL
OF FUTURES MARKET TRADING ACTIVITY

By

Robert M. Behr

June 1981


Chairman: Ronald W. Ward
Major Department: Food and Resource Economics

A futures market is an institutional arrangement which evolves in

response to commercial need. Futures markets facilitate forward exchange

in a commodity through hedging activities conducted by commercial inter-

ests. In addition futures markets play an important role in the inter-

temporal allocation of stocks. More recently the forward pricing function

of these markets has been emphasized with the emergence of nonstorable

commodity futures markets. Hence, the futures market is more than just

a place for trade futures contracts. Rather, it is an institutional

arrangement that is an integral part of the broader economic environment

in which the economic activity of a commodity transpires.

It was suggested that futures markets evolve or respond to commer-

cial needs. Since the needs of commercial interests are affected by the

economic environment, it follows that futures market activities will re-

spond to the changing economic forces. One example of a changing eco-

nomic environment is the change in market structure. Since the post-World










War II era there have been significant changes in market structure in

many industries including agriculture. Another economic condition sub-

ject to change is the influence of government in commodity markets

through price support activities.

A futures market beset by a changing economic environment will be

affected through its level of use. A net benefit model was proposed that

relates market net benefits to hedging, speculation and volume. The

levels of these variables which maximize net benefits are optimal. Since

the economic environment of markets varies cross-sectionally and inter-

temporally, the optimal level of hedging, speculation, and volume may

differ as well.

Market power and firm size were two structural variables which were

argued to influence futures market activity. The government's role in

assuming commodity risk through price support activities was also thought

to be influential. Other variables addressed at a conceptual level in-

cluded price risk, basis risk, and the maturation process of a futures

market. The conceptual net benefit model incorporated the aforementioned

exogenous effects. A conceptual analysis, which showed the partial

effects of these exogenous variables on hedging, speculation and volume,

was conducted. The partial effects, in turn, were the basis for the

hypotheses derived from the conceptual model.

A mathematical model was operationalized from the first order con-

ditions of the theoretical net benefit function. The model consisted

of three equations corresponding to three endogenous variables, hedging,

speculation, and volume. Fourteen explicitly defined exogenous variables

were included in the model. The exogenous variables which were used re-

late directly to their respective theoretical counterpart. A logistic











functional form specification was used to describe the behavioral

relationships.

An econometric model was developed to estimate the parameters of

the mathematical futures trading model. Pooled cross-section and time

series data were used in the simultaneous equation model. Since the

equations were each overidentified, an alternative to the Ordinary Least

Squares estimator was desired because the estimates would not be con-

sistent. Other problems including unequal observations on each cross-

section, variance-components and contemporaneous correlation were

addressed. An estimator which systematically dealt with these problems

was developed and utilized to generate estimates of the structural

parameters.

A major finding of this research is that industrial concentration

has a quadratic effect on hedging activity. The importance of this

finding is that it suggests the causal linkage between futures market

activity and market structure. Empirical evidence suggesting that

price supports reduce the need for hedging was shown. Markets were

also observed to have a very long maturation process. A conclusion of

this research is that futures markets evolve in response to the needs

of hedgers which, in turn, are shaped by the dynamic forces of the

economic conditions that beset commercial interests.


xiii
















CHAPTER 1

INTRODUCTION


A futures market like most markets facilitates exchange. In the

case of the futures market, contracts are the object of exchange. The

contract is a highly standardized instrument that is traded on the

exchange floor according to the rules and regulations set by the ex-

change. All contracts dated similarly for a specific commodity are

identical, thus reducing the importance of buyer-seller identity. Im-

personal trade is further enhanced by the exchange's backing of all

transactions. Market entry and exit is aided via the nominal cost of

transacting. In this context the futures market resembles the con-

ceptual model of perfect competition.

An organized futures market is a voluntary association of traders

who have joined together for the purpose of exchanging futures contracts.

This association constitutes the members of an exchange. Membership is

typically referred to as having a seat on the exchange. The seats can

be sold through buyer-seller negotiations subject to membership approval.

Aside from determining the integrity of potential members, the exchange

is principally involved in writing the contract to be traded, establishing

the rules which govern trade, and enforcing all rules and regulations.

The main objective of the exchange is to provide and regulate a market

place so that members and their clients can trade futures contracts for

a specific commodity [Hieronymus, 1971].










The futures contract is a highly standardized contract that repre-

sents a commitment to make a future transaction. A commitment to buy

(a long futures position) or a commitment to sell (a short position) need

not be filled with commodity transfer. Generally the commitments are

offset by entering into an opposite futures position which negates the

previous open position. The typical futures contract is standardized

with respect to the following dimensions:

1.) quantity to be delivered

2.) quality acceptable for delivery

3.) place of delivery

4.) time of delivery

5.) terms of payment

6.) recourse in case of injury or settlement of disputes.

All contracts are identical with respect to the above dimensions. A

transaction involves agreement upon the contract price and the number

of contracts exchanged. The standardization forces the focus of buyer-

seller interaction to the discovery of the futures price.


The Analogy with Money

The development of futures markets in this country has paralleled

the development of forward marketing. Forward dealings or forward con-

tracting have a history as old as mercantile trade. In the U.S. forward

contracting is traced back to the grain trade of the mid 19th century.

In an effort to avoid price risk grain merchants holding grain over the

winter months contracted ahead to sell grain to dealers in midwest

terminal markets. Exchanges were established to facilitate this type of

trade as well as the spot trade. The development of means of










standardization enhanced forward trade because it enabled lower trans-

actions cost in the contract negotiations. Eventually, the futures

market evolved promoting commercial trade in forward dealings by attrac-

ting hedging interest as well as a speculative interest.

The futures contract is a special case of the forward contract. A

forward contract is an agreement between buyer and seller to transfer

title according to the stipulations of the contract. The contract

stipulations are arrived at mutually through a buyer-seller bargaining

process. On the other hand futures contract stipulations with the

exception of price are written by the exchange. The futures contract

is traded on the floor of an organized exchange whereas the forward con-

tract is not. For these reasons futures contracts can be obtained at

a fraction of the transactions cost required of a forward contract.

However, there is a limit to its commercial usefulness; namely, the more

standardized the forward contract the less applicable a contract is to

broad-based commercial use. This tradeoff helps to explain why both

forms of marketing tools exist in the commercial trade of a commodity.

The evolution of futures markets parallels the development of money

exchange as it evolved to replace barter exchange. In a highly special-

ized economy the transactions costs associated with barter exchange would

be considerable. Taking one's wares from door to door to exchange for

other consumptive items would doubtless be burdensome. An open market

under such a system would also be very confusing. The number of exchange

ratios required to facilitate trade is on the order of N(N 1)/2, where

N is the number of goods and services exchanged in an economy. Hence,

the value of money as a unit of account is quite evident in an advanced

economy.










The use of money depends on its acceptance as a standard of exchange.

Money can represent a temporary abode of purchasing power if it is

accepted as payment in exchange for goods and services. In the U.S. the

dollar is acceptable nearly everywhere as a means of payment in an ex-

change. The dollar is as good as its federal backing, although money in

an economy need not have federal backing. Nonetheless, the central

government backing makes the government a third party to all transactions

and a point of legal recourse to injured parties of a transaction.

The similarities between a futures market with its antecedent,

forward contracting, and money exchange with the barter system it re-

placed are striking. The futures contract is a highly standardized

commitment to transfer commodity ownership at a future date. It repre-

sents a standard of exchange in terms of the commodity. The standard-

ization reduces the transactions cost of forward dealings since all

contract attributes other than prices are predetermined. Like money

the standardization of forward dealings is useful to the extent it is

acceptable as a means of forward contracting. Moreover, the futures

contract must have the potential for broad acceptance as a substitute

for a merchandising contract. If it lacks broad acceptance the costs

of organizing a market to exchange futures contracts may outweigh the

benefits.


Recent Trends

Organized commodity futures trading has existed in the U.S. since

the middle of the 19th century. Initially only a handful of agricultural

commodities were traded on futures markets. Today a wide assortment of

commodities have active futures markets. In the past two decades futures










trading began in commodity groups as diverse as foreign currencies,

financial instruments, and petroleum products. New markets were also

established for agricultural commodities such as live cattle, live

hogs, frozen pork bellies, and frozen concentrated orange juice (FCOJ).

Currently, there are 70 contract markets as designated by the Commodity

Futures Trading Commission (CFTC), the regulatory body that oversees

futures trading in the U.S.

Since 1960 the volume of trading on U.S. markets has increased more

than tenfold (Table 1.1). Some of that increase can be attributed to

the development of new markets, such as the livestock products markets

and the precious metals markets. It is also apparent that the volume

increased significantly on the established markets as well. For in-

stance volume on markets in the grains group was 23.1 million contracts

in 1978, an increase of nearly 20 million over an 18 year period. In-

cluded in the grains group are commodities such as wheat and corn which

have been traded on futures markets since 1880. Despite the long history

of these markets, volume records were set practically on an annual basis

over the last two decades. The volume of trade in corn futures jumped

dramatically from 336 thousand in 1960 to 6.2 million contracts in 1978.

Hence, futures market activity as measured by the volume of trade has

increased significantly over a broad range of futures markets including

both old and recently developed markets.

Another indication of the increased activity on futures markets is

the jump in the level of open interest. Average monthend commitments

for all regulated markets increased from 149 thousand in 1960 to 1.37

million contracts in 1978 (Table 1.2). The increase in open interest

reflects increased commercial (hedging) and speculative use. Like the











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volume statistics, open interest has increased across all commodity

groups. Since futures traders (i.e., hedgers and speculators) desire

to hold open interest, the statistics suggest the presence of dynamic

forces which have affected hedging and speculative behavior in a

significant manner.

Several exogenous factors that have influenced hedging and specu-

lation are readily identifiable: they include the lessened role of

government in price support activities, the expansion of export markets,

the change to flexible exchange rates, and the effect of the huge in-

creases in the price of oil. Since these factors have the effect of

increasing supply and demand uncertainty, commercial interests un-

willing to bear increased commodity risks seek marketing strategies

such as futures trading to reduce that exposure. On the other hand

speculators are attracted, according to the traditional view, because

of their willingness to bear such risks. Consequently, periods of great

price uncertainty should give rise to increased use of futures markets

(i.e., an increased level of open commitments). Using the coefficient

of variation statistic constructed from monthly cash prices of selected

agricultural commodity markets as a measure of price uncertainty, it

is apparent that commercial interests are facing greater levels of un-

certainty relative to the early 1960s (Table 1.3). Thus, it seems that

a partial explanation for the increased hedging and speculative activity

is the increased uncertainty across many commodity markets.


Economic Functions of Futures Markets


Futures markets are often justified because of their role in risk re-

duction. Commercial interests offset price risk in the cash market with





















Table 1.3--Intertemporal movements in the coefficient of variation (CV)a


Year Wheat Corn Cotton CLive Por Eggs
Cattleb Belliesb E

1963 2.5710 5.7749 1.3817 -- -- 13.4244

1968 4.5339 5.0974 5.5715 6.2582 9.9787 5.4588

1973 17.0091 21.6141 18.1765 12.1765 12.2871 20.3372

1978 14.6620 12.6953 7.0564 13.1787 15.4263 7.8154


12
aCV ( p (Pi
i=1


- p)2)1/2


()-l1


The 1968 figures are actually


(pi = average price of commodity j
in month i)


based on 1969 data.


Price information


was not obtained for these markets prior to 1969.










an opposite position in the futures market. This practice is commonly

called hedging. In contrast, speculative involvement in futures has

drawn repeated fire over the years because of its association with the

moral question of gambling. However, speculators play an important role

in the risk reduction role of futures markets. The futures market

facilitates the separation of commodity ownership from the production,

storage, transportation, and processing functions associated with the

commodity [Paul and Wesson, 1960]. Although this separation does not

occur in fact, it does have the effect of permitting hedger specializa-

tion in the production, storage, and processing of the commodity with-

out the marketing problems brought about by price uncertainty. On the

other hand the implicit separation allows speculators to assume the

risks of ownership without the handling of the commodity. Lower costs

in commodity handling because of reduced risk increase the efficiency

of commodity market with an end result of lower prices at the consumer

goods level.

Working [1953] has argued that hedging in futures is not motivated

by the desire to reduce risk. Rather hedging is conducted to facilitate

buying and selling decisions, to allow for greater freedom of business

action, and to serve as a basis for conducting storage of commodity sur-

pluses. Working adds that business risks are likely reduced as a con-

sequence of conducting hedging activities for one of the above reasons.

Nonetheless, the hedging mechanism enhances the marketing efficiency in

commodity subsectors as the commodity passes from production to con-

sumptive use. In a period of rising marketing and distribution costs

the futures market can serve as a countervailing force especially for

agricultural markets where this problem has been pervasive.










The futures market is also a source of equity capital. Often loans

to agriculturalists are administered contingent on marketing programs

that do not carry a great deal of risk. Hence, appropriate hedging

strategies are implemented to secure outside funds. The futures market

indirectly causes specialization in the ownership and control of capital.

It allows hedgers to control capital without owning capital. As such,

resources can be put to their most productive use, increasing the

efficiency of the commodity complex.

At an aggergate level the futures market is influential in the

allocation of stocks over time. In the absence of a futures market

stockholders would have to rely on their own price expectations and

other sources of information to guide stockholding decisions. Under

this scenario stockholders face increased uncertainty causing them to

carry less inventory since marginal storage costs are responsive to

increased risks. Consequently, a greater intraseasonal price range

would be expected under a no futures market scenario. There is some

empirical support for this view [Tomek and Gray, 1970].

In theory, the difference between the futures and cash price,

commonly called the basis, is equal to the price of storage in the

analysis of storable commodities. If the basis exceeds the cost of

storage stockholders can engage in arbitrage activities by simultaneously

taking a long cash position and an equivalent short futures position.

Given fixed storage costs price changes on either the futures or cash

market represent signals to stockholders to alter their inventories.

Since speculators are willing to bear the price risks of commodity

ownership, more of the commodity supply will be carried through time.

Hence, the effect of the presence of futures trading on the intertemporal

allocation problem is readily apparent.










For nonstorable commodities there is no allocation problem. However,

the difference between the futures price of a transformed commodity and

the cash price of a raw commodity input reflects a market determined

price of services rendered [Paul and Wesson, 1967]. Basis changes indi-

cate shifts in the supply and demand for such services. If there is an

aberration between the basis and the cost of providing services,

arbitrage opportunities would exist.

Futures markets facilitate a discovery process that is designed to

yield competitively determined price. They bring the relevant information

to bear on the price discovery process of a commodity that is dated.

Thus, the futures price is a source of information regarding future

supply and demand conditions. In theory, it is an unbiased and efficient

estimate of the future spot price [Tomek and Gray, 1970]. As such,

commodity activities that are dated, such as storing to a future date,

benefit from the availability of this sort of information. This has

particular relevance for agriculture in the wake of the increased price

uncertainty on agricultural commodity markets. In summary it is clear

that futures markets play an important role in the economic activity of

commodity markets. The role of the futures industry in agriculture is

significant given the range of agricultural commodity futures markets

(Table 1.4).


Economic Problems and Concerns

The futures market is an institutional arrangement within the con-

text of a commodity complex. A commodity complex is defined to be the

structural, institutional, and legal settings which govern the economic

activity from commodity production to consumption. Over time these












Table 1.4--Contract market designations: the agricultural markets


Exchangesa Exchanges
Commodity Commodity
Market With Trading Market With Trading
In In


Wheat

Corn

Durum

Oats

Rye

Ba rl ey

Grain
Sorghums

Soybeans

Soybean Oil

Soybean
Meal

Flaxseed

Cotton

Eggs

Butter


MACE, CBOT, KCBT, MGE

MACE, CBOT, KCBT, MGE

MGE

MACE, MGE, CBOT

MGE

MGE


KCBT, CME

MACE, MGE, CBOT, KCBT

CBOT


CBOT

MGE

Cotton

CME

CME


FCOJ

Live Beef

Feeder Cattle

Boneless Beef

Imported Beef

Live Hogs


Pork Bellies

Skinned Hams

Turkeys


Iced Broilers

Plywood

Stud Lumber

Lumber

Potatoes


Chicago Board of Trade
Chicago Mercantile Exchange
New York Mercantile Exchange
Mid-American Commodity Exchange
Minneapolis Grain Exchange
Kansas City Board of Trade
Cotton Exchange


Cotton

CME, MACE

CME

CME

NYME

CME, MACE


CME, MGE

CME

CME


CBOT

CBOT

CBOT, CME

CME

CME, NYME


aCBOT
CME
NYME
MACE
MGE
KCBT
Cotton










settingswill make dynamic adjustments. Accordingly, the futures market

is beset by an everchanging environment. Since the futures market is

utilized by the economic agents of the commodity complex the futures

market cannot be immune from a dynamic enviornment.

At this writing there is very little information regarding the re-

lationship between a futures market and its environment. This is

particularly bothersome for several reasons. In the agribusiness in-

dustries as in most industries there has been a trend toward larger

firms and a decrease in firm numbers. In the wheat milling industry,

for instance, the number of firms has dropped from 703 in 1958 to 340

in 1972. Industrial concentration, which measures the size of the

biggest firms relative to the industry, has increased over many agri-

cultural industries. The SIC four firm concentration ratio for the

soybean milling industry has increased from 37 to 53 percent from 1958

to 1972. These numbers suggest that some commodity markets may not be

as competitive as once perceived.

The problem with these trends is that futures markets depend on the

competitive spot markets according to the traditional view. If non-

competitive elements are present in the price discovery process, then the

role of the futures market can be questioned. Since futures contracts

are rarely fulfilled by delivery, it is imperative that hedgers execute

futures transactions simultaneously with cash market transactions without

causing a price effect. This insures a minimum exposure to price risk.

However, with the presence of large cash positions backed by futures

positions the possibility of a squeeze is heightened. Distortions often

ensue which reduce the hedging effectiveness of futures markets [Paul,

1976].










The lack of research concerning the effect of concentration on

futures markets results from the general lack of research in the insti-

tutional area of futures market study. A typical question in this area

of study relates to the existence of futures markets for some commodities

while most commodities do not support futures trading. Some authors have

attempted to delineate the set of conditions which are deemed necessary

for futures market evolution. See Gray [1966], Hieroynmus [1971], Paul

and Wesson [1960], and Telsar and Higinbotham [1977]. The conditions

which are most often cited include: 1. many buyers and sellers of the

commodity; 2. the commodity must be gradeable and subject to standardiza-

tion; 3. the commodity should not be too perishable; 4. the supply must

flow naturally to market, not hampered by artificial constraint such as

governmental actions; and 5. there must be price uncertainty. Hieronymus

[1971] argued that it is difficult to impose a rigorous set of eligi-

bility characteristics when one considers empirical evidence. For

example, perishable commodities such as live cattle and live hogs have

supported active futures since their establishment in the mid 1960s.

In the FCOJ processing industry the number of processors was relatively

low in 1978. Nonetheless, the FCOJ futures market is reasonably well

used by processors and speculators alike.

From a public policy perspective the apparent void in the institu-

tional area of futures market study is hazardous. An obvious agricultural

policy that has a direct bearing on futures market activity is the price

support programs. When government price supports are in effect the

commodity price risk is borne in part by the public. Consequently, the

need of futures trading is virtually eliminated. Whether the benefits

of support policies outweigh the benefits of a market which is credited










with several important economic functions is a matter for a public forum.

Unfortunately, public discussion on these matters is constrained because

of the lack of research in the institutional area of futures markets.

Another area of public discussion which may impact upon futures markets

is antitrust policy. To the extent that large firms affect futures

market activity it is clear that Federal Trade Commission policy can

affect activity on a futures market serving an affected large firm.

Establishment of futures markets by private interests could be

enhanced if the environmental effects were better understood. In view

of the economic benefits of a futures market, government policy might

be directed at a private sector means of dealing with income and price

uncertainty problems as well as the problem of resource allocation.

Incentives could be provided for the establishment of markets that were

previously thought too risky to establish. Initial costs are typically

high which may be a big disincentive, because it may take several years

before active use ensues. Among the newly established markets there

appears to be a maturing stage during the early periods of the market's

existence. In the case of pork bellies the contract had to be rewritten

to encourage commercial use [Powers, 1967]. After several years of trial

and error the pork belly market finally became widely used. Other markets

have not been so fortunate. The role of government in futures market

development could be important in these later cases. Any kind of in-

volvement, however, depends on a better understanding of the institutional

area of futures markets.







17

Statement of Objectives


(1) Develop the conceptual net benefit model characterizing

futures market behavior in terms of hedging open interest, speculation

open interest, and volume.

(2) Investigate the cross-sectional and intertemporal influences

which affect futures market behavior. Among the influences that will be

investigated include market structure, government involvement in commodity

markets, price risk and commodity perishability.

(3) Hypothesize the behavioral effects of dynamic adjustment in
the variables which affect hedging, speculations and open interest.

(4) Develop an econometric model which can be used to test the

hypotheses made in (3).

(5) Present the results showing the partial and total effects of

the exogenous variables on hedging, speculation and volume.

(6) Discuss the policy and research implications which emanate

from this work.


Methodology

The futures market is an evolving institution which is subject to

the vicisitudes of its environment. The view that the futures market is

a "given" is appropriate only for short run analysis. With the trends

in the structural make-up of the agribusiness industry there are many

concerns over the future of the futures industry. In the research which

follows this problem will be addressed. A theoretical model will be

proposed which characterizes the behavior of futures markets over time.

The specific behavior implied is the activity that transpires in a

market setting (e.g., volume and open interest). Hypotheses regarding










the environmental impacts that beset the institutional setting of futures

market activity will be made.

A theoretical model of futures market activity is designed to

facilitate an econometric investigation of futures market activity. An

empirical approximation of the theoretical model will be proposed and

subjected to statistical analysis. The data used for this investigation
1
are drawn from a time series of observations covering 16 commodity markets.

Since this research is oriented towards agriculture the markets included

are agricultural markets, only. However, the included markets differ

significantly, in some cases, with regard to the structural makeup of

their respective industries. This cross-sectional variation is useful

because the short nature of the time series, 1961 to 1978, impedes the

investigation of the structural effects.

Briefly, the econometric model that will be proposed is a three-

equation model with hedging, speculation and volume appearing as en-

dogenous variables. The exogenous variables of the model are the

empirically defined influences which were suggested above. Obtaining

the parameter estimates of the structural equations will facilitate the

hypothesis testing. However, there are several econometric problems which

make straightforward Ordinary Least Squares( OLS) estimation of the re-

duced form equations inappropriate. Consequently, an alternative esti-

mator is developed and applied to the estimation of the structural

parameters. The estimator resembles the three-stage least squares (3SLS)

estimator, but has a modification which results from the pooling of

cross-sectional data. A fuller discussion of this is deferred to Chapter 5.



See Appendix A for a list of these markets.










Overview


Chapter 2 will be a brief look at the literature that predates the

current research. In the subsequent chapter the theoretical model of

futures market behavior will be proposed. Chapter 4 will be the opera-

tionalization of the theoretical model. A separate chapter will be

devoted to the econometric analysis and its associated problems. This

will be followed by a presentation of the statistical results and a

discussion of the implications. In the last chapter a summary and

conclusion will be given.















CHAPTER 2

LITERATURE REVIEW

In this chapter a review of the literature that is pertinent to the

subsequent analysis will be presented. The literature is categorized

into three groups: (1) the historical and evolutionary aspects; (2) the

theories of hedging; and (3) the theories of speculation. Following the

discussion on this literature is a section devoted to recent papers by

Telser [19801 and Telser and Higinbotham [1977]. The models emanating

from these papers represent a pioneering attempt at modeling futures

market behavior. Since the current analysis is an extension of this

work a separate section that summarizes this work, is included.


Overview of Futures Market Literature

Historical and Evolutionary Aspects

Futures markets evolved from merchandising trade that was already

in existence [Hieronymus, 1971]. Irwin [1954] observed that futures

contracts grew out of time contracts that were used in the merchandising

of grains. Grain merchants often held inventories into the spring be-

cause the unnavigatable rivers prevented delivery to terminal markets.

Time contracts more commonly called forward contracts were developed

as a means of forward pricing inventories reducing the risk of adverse

price movements while the stocks were held. Futures markets were orga-

nized to facilitate this sort of merchandising trade ". by providing

uniform rules governing the transactions; standards of grade, quantities,










and delivery terms; and clearing arrangements whereby the clearing house

guaranteed each contract" [Gray and Rutledge, 1971, p. 59]. Initially

the futures market resembled an organized forward market where most

contracts were settled by delivery. As time passed, it became apparent

that futures contracts were best used as a temporary substitute for a

merchandising contract. Merchants held future contracts until a

merchandising contract or cash market trade, which suited specific needs,

was made. Consequently, most of the contracts were fulfilled by an off-

setting trade rather than delivery. The futures market did not replace

the forward dealings present at this time; rather it complemented exist-

ing trade.

For a long time after trading has begun it was thought that futures

trading was a speculative devise. One important fact that furthered this

thinking was the lack of contract fulfillment by delivery. To some the

futures market was essentially a "paper" market not serving any real

economic function. However, beginning in the 1930's several published

works changed the speculative focus of futures markets. Hoffman [1932]

was perhaps the first to observe that open interest had a seasonal

pattern that corresponded to the seasonal pattern of stocks in the grain

markets. If the markets were oriented towards speculation then the

open interest commitments should reach peak levels just prior to

harvest, the period of the greatest uncertainty [Gray and Rutledge, 1971].

This was simply not the case as Hoffman observed., Irwin [1954] furthered

this view in a study of the butter and egg markets which also showed

seasonal patterns of commitments that complimented inventory accumula-

tion and displacement.










The reason that commitments varied seasonally with stocks is that

futures markets were used by hedgers to protect inventory values. The

difference between the spot and futures price reflected a market deter-

mined carrying charge [Working, 1949]. As such stockholders could

arbitrage the two markets and earn the carrying charge as a return for

providing the storage service. The hedging business during the early

years of futures trading was primarily the inventory-type hedge. As

the statistics on commitments were accumulated the importance of the

inventory hedging became more visible. Short hedging commitments rose

to a seasonal peak as stocks were being built-up. With Working's price

of storage theory the ". inventory hedging view of futures markets

had its culmination with the demonstration that the cash-futures

price differential reflected a true price of storage for continuous

inventory commodities" [Gray and Rutledge, 1971, p. 59). Thus the

question of whose market was apparently settled nearly a century

following the advent of futures trading.

Although Working [1953, 1954] and others made persuasive arguments

that the futures markets evolved to meet hedgers needs, the importance

of speculative involvement was not ignored. Working observed during

the harvest period as stocks were being augmented there was a signifi-

cant imbalance between short and long hedging. This owes to the fact

that the futures markets for storable commodities were dominately used

as an inventory hedging vehicle, as mentioned earlier. To facilitate

contract holding by short hedgers, long speculation was required to buy

the contracts. Needless to say, speculative involvement flourished as

speculators jumped to take advantage of profitable opportunities on the

expectation of a favorable price move. In the inventory hedging markets










it was observed that changes in long speculation paralleled the movement

in net hedging which is defined as the number of contracts held by short

hedgers less the number held by long hedgers. This gives support to

the hypothesis that speculators accommodated hedger's transactional needs

in futures trading. It is conceivable that a market could exist having

balanced hedging and sufficient volume without speculative activity.

However, commercial interests have forward dealings that in general do

not coincide. Thus, an important role of speculative activity.is to

carry commodity risks facilitating the coordination of the intended

exchange.

In time new futures markets were established for nonstorable

commodities such as fresh eggs. Previous to the writing of a fresh

shell egg contract there was extensive use of the storage egg contract

by warehousemen [Gray and Rutledge, 1971]. The seasonal pattern of

egg production required stocks to be carried through the winter months.

Consequently, the storage egg contract provided a means to conduct in-

ventory hedging. Technological improvement in the production of eggs,

however, has all but eliminated the seasonality of egg production as

current production moves directly into consumptive channels. The

commercial need for a storage egg market dissipated which ultimately

led to the cessation of trading in storage eggs. The emergence of a

fresh shell egg contract and its continued success was a shock to many

economists who believed the futures market was not suited for trade in

nonstorables. This belief was fostered, in part, because intertemporal

price spreads for nonstorables was not well understood unlike the case

of the storable commodities.










The success of the fresh shell egg contract was followed by other

perishable commodities such as live cattle, live feeder cattle, and live

hogs. To explain the existence of these futures markets, new theories

were needed to supplant the narrow view that markets were supported by

the inventory hedging. It became clear that producers could forward

price planned production through the use of futures. Processors, on the

other hand, could cover forward sales or fill anticipated requirements

with a long futures position thereby fixing a processing margin [Gray

and Rutledge, 1971]. In the feeding of cattle, for instance, the

futures contract can be used to forward price the feedlot services.

Paul [Paul and Wesson, 1967] has argued that the spread between the

cattle futures price and the price of its inputs reflects the price of

feedlot services. This is analogous to the price of storage argument

for the storable commodities. Thus, the futures market can serve the

interests of those dealing in nonstorable, as well, which partially

explains the existence of such markets.

The degree of perishability is one factor which was thought to

affect the evolutionary process of futures markets. There are other

factors which affect this process. Powers [1967] has shown the impor-

tance of the contract terms in an empirical study of the pork belly

futures market. Often markets fail to attract sufficient speculation.

Speculation is credited with enhancing liquidity which reduces the

cost of hedging. For instance, see Gary [1966; 1967], Working [1962]

or Ward [1974]. Several published articles have dealt with the impor-

tance of liquidity in market development [Gray, 1966]. There has been

some discussion concerning the effects of market structure on futures

market activity [Paul and Wesson, 1960]. However, empirical studies

dealing with the structural question cannot be found in the literature.









Hedging Theories

The theory relating to hedging has matured significantly over the

past 50 years. Initially, hedging practices were thought to be analogous

to insurance [Blau, 1944]. In an effort to eliminate price risk commer-

cial interest could enter a futures position that was equal and opposite

to the cash position. An implicit assumption of this view of hedging

was that the cash and futures price moved in an exact parallel corre-

spondence. As such a loss in one market was protected by a gain in the

other. This view of hedging is implicit in the normal backwardation

view of intertemporal price movements [Keynes, 1923; 1930]. Hedgers

are willing to pay a risk premium in order to eliminate price risk.

Consequently, futures markets with a preponderence of short hedgers

would have a downward biased price.

The price insurance theory was replaced by a "risk reduction"

theory [Gray and Rutledge, 1971]. It was observed that cash and futures

prices do not move in perfect correspondence. Although cash and

futures prices are not perfectly correlated, there is a strong tendency

for the prices to move together. Moreover, the variability in the

basis is usually less than the cash price variance. Thus a hedged

position is often less risky than an unhedged position. Markets with

low basis variability relative to cash price variability are considered

to be better hedging markets because of the enhanced risk reduction

capability. There are several empirical studies that have investigated

the hedging effectiveness of futures markets in terms of its ability

to reduce risk [Dale, 1981 and Ward and Schimkat, 1979].

Gray and Rutledge [1971] make the point that many studies are vague

with regard to the definition of risk. They surmised from the studies









that risk is taken to be the "statistical expectation of a unit loss."

As such the marginal risk is constant which implies the possibility of

negative risks. More recently, studies have incorporated the behavior

of individuals faced with uncertainty in a more explicit manner. That

is the assumptions regarding the decision rule under uncertainty are

expressly stated.

One major theme throughout the published research of Working [1962]

is the deemphasized role of risk in the motivation to hedge. Working

argued that futures markets facilitate buying and selling decisions,

etc. (see Chapter 1). In addition Working suggested the simultaneous

nature of the decision to hold stocks and futures commitments. For

example, if a favorable basis movement was expected it might prompt

stockholders to augment inventories and enter a corresponding futures

position. Otherwise, the decision to accumulate stocks might be delayed.

This view was a significant departure from the notion that the decision

to hold stocks and the decision to hedge were independent processes.

Several classes of hedgers are identified by Working. One such

class is termed selective hedging which might occur if ". .. a merchant

places high subjective probability on a price rise in the next period:

he may then leave all or part of his inventory unhedged" [Gray and

Rutledge, 1971, p. 83). Anticipatory hedging is another class identified

by Working. Anticipatory hedging refers to the hedging of anticipated

forward commitments. This broadened view of hedging has influenced the

promulgated regulations regarding legitimate hedging practices. In 1968,

for instance, cattle feeders were exempted from limits on long corn

positions as well as other feed-grains because the hedging of the feed-

grain requirements represents an example of an anticipatory hedge in the

sense that Working had expressed.










Another approach to a general theory of hedging has been developed

using the portfolio analysis approach. The alternative futures market

strategies are equivalent to the assets of a portfolio. The return on

each asset is assumed to be a random variable and has an associated sub-

jective probability distribution. It is assumed that the hedger has

a concave, cardinal utility function. The utility function says that

the expected value of the portfolio is some function of the respective

asset distributions. Maximization of the function, or the expected

value, is the assumed behavior by hedgers and speculators alike. Use

of the portfolio analysis and the related mean-variance approach de-

veloped by Markowitz in empirical research has been common [Rutledge,

1972; Peck, 1975]. The beauty of these conceptual models is the rigor

employed in their development. These models do not necessarily contra-

dict the earlier work of Working and the risk-reduction advocates. In

fact, Rutledge [1972] has shown the compatibility of arbitrage (a de-

scriptive term of Working's theory of hedging) and risk reduction type

hedging via the portfolio model. This is not so surprising in that

Working suggests that arbitrage-type hedging reduces business risks

however defined.


Speculation Theories

As stated earlier it was initially thought that providing the means

for speculation was the role of futures markets. There has been a history

of legislative moves to ban futures trading because of the so-called

speculative nature of futures markets [Hieronymus, 1971]. One of the

first theories of speculation suggested that speculators provide a

service to hedgers by bearing commodity risks. Keynes [1930] argued

that speculators earn a risk premium as payment for such services.










This concept is tied to the price insurance hedging theory which evolved

simultaneously. The theory of normal backwardation was a consequence of

the risk premium-price insurance view of futures markets. Alluded to

earlier, the theory of normal backwardation suggests that the current

futures price is discounted to the expected cash price. The discount is

the risk premium which Keynes argues must be present to attract specula-

tion.

An implication of the theory of normal backwardation was the intra-

seasonal futures price movement. In markets dominated by short hedgers

it follows that the futures price is downward biased. As such the futures

price would in general rise. Keynes argued that consistent long specu-

lative positions would yield profits in the long run.

Working's [1958] theory of anticipatory prices, which states that

futures prices react instantly and fully to new information, casts doubt

on the normal backwardation view of speculative price movements.

Working's theory suggests that futures price changes are random because

new information occurs randomly. A price that is biased in an efficient

market cannot persist because of profit or arbitraging potential.

Working's theory of speculative prices evolved in conjunction with his

work on hedging theories. Working viewed the speculator as a necessary

agent to a well-functioning futures market because of the liquidity

provided.

The speculative price theories emanating from the work of Keynes

and Working have been put to empirical tests of one kind or another

since the 1920's. A popular test that evolved sought to determine

whether or not speculators could earn systematic profits as Keynes

suggested. Empirical research using various sorts of simulated trading










plans have yielded mixed results. See Stewart [1949], Houthakker [1957],

and Rockwell [1967]. Some have observed that large speculators make

profits while small traders generally lose. The profitability of specu-

lative positions also has implications regarding price stability.

Friedman [1953] has argued that profitable speculation has a stabilizing

effect on cash price.

Another type of empirical test developed was a statistical investi-

gation of the randomness of futures price changes. Working's theory

implied that the price changes on organized futures markets conformed to

the random walk hypothesis. A significant number of studies have in

fact rejected this hypothesis which implies some systematic price

behavior. See Working [1954], Houthakker [1957], and Smidt [1965].

This suggests the presence of bias. However, a number of studies argued

that systematic short run futures price behavior was not supportive

of the Keynes theory of normal backwardation [Gray and Rutledge, 1971;

Gray, 1967].

The view that speculation is primarily important for its impact on

liquidity is now welT-entrenched. The importance of liquidity is

evident when one considers the bivalance of market price. Working [1962]

observed in a market with a small number of buyers and sellers that

the difference between the bid and ask prices was magnified. As the

sample of bids and offers increases in a market setting there is a greater

likelihood that this spread will narrow. A hedger, having a position in

a futures market that is characterized by a small number of traders,

may have to take a price concession by offering to sell at the lower bid

price or asking to buy at the higher ask price in order to expedite a

trade in conjunction with business activities. The virtue of futures










markets is that they allow commercial interests a great deal of flexi-

bility with regard to forward dealings. If price concessions must be

taken to expedite a futures trade then an additional cost is incurred.

Working [1953] has termed this the hedging cost which is distinguished

from commission charges and the opportunity cost associated with margin

requirements.

The need for speculation arises immediately when one recognizes

that commercial forward dealings may not coincide. Further, there may

be more short hedgers than long hedgers using a given commodity futures

market. As such, at any given time there may be an imbalance in hedging

needs. Speculation serves to smooth the incongruities in hedging needs

[Gray, 1966; 1967]. If the market attracts sufficient speculation, then

hedging costs are held to a minimum. Working [1953; 1954] points out

that a threshold must be passed in developing markets to ensure their

survival. That is, hedging and speculation are not enough for the

existence of a successful futures market. The implication is that

markets require a certain degree of liquidity to guarantee survival.

This point is supported by other theorists as well [Gray, 1966; Telser

and Higinbotham, 1977]. It must be recognized, however, that there is

nothing special about speculation that enhances liquidity. A market

with a sufficient number of short and long hedgers with congruent forward

needs would likely be fairly liquid. The point is that the later event

is not likely to occur. Hence, there is a need for speculation in

futures markets.

Liquidity is a term that has been used quite freely in the dis-

cussion of market liquidity. A rigorous definition of liquidity is not

apparent in the literature. Several have developed what is called a










speculative index which is used as a proxy for liquidity [Ward, 1974;

Working, 1962]. Ward argues that illiquid markets (i.e., a low

speculative index) can lead to a bias in the basis of FCOJ market.

Gray [1967] observes a similar effect in other so-called illiquid

markets. Liquidity is often implied by the level of volume observed in

a market place per unit time [Telsar and Higinbotham, 1977]. If the

volume is very low the likelihood that the transactions price is the

true equilibrium price is lessened. This follows because the small

number of traders may not be a representative sample of the population

of traders. Hence, all of the relevant information is not focused on

the price discovery process. A symposium addressing pricing problems

in agricultural commodity markets with a special emphasis on thin or

illiquid markets is enlightening but does not offer a rigorous defini-

tion of liquidity [Hayenga, 1979].


Telser's Model

In more recent work in the area of futures markets Telser has put

forth a theory of organized futures markets. Briefly, the theory pur-

ports to explain the evolution of futures markets as an institutional

arrangement in commodity markets. Telser argues that the advantage of

futures markets over other means of forward exchange is the low trans-

actions cost which is a consequence of market liquidity. Telser adds

that the futures market evolves if the benefits of its organization

exceeds the costs. This may be self-evident but Telsar utilized the

notion of net benefits to construct a mathematical model of futures

market behavior. A closer look at Telser's model follows directly

[Telser and Higinbotham, 1977].










A Model of Liquidity

Telser observes that futures markets are beneficial because they

are liquid. As a centerpiece to the model of futures market behavior,

a theory of the distribution of market clearing prices is presented to

account for market liquidity. The results of this theory suggest that

the asymptotic distribution of market clearing prices is normal. In

addition the theory suggests the determinants of market liquidity.

Analysis of the distribution of market clearing prices begins with

the assumption that traders observed in a market on any given day are

a random sample from a population of traders. Potential buyers have a

distribution of maximal acceptable bids and potential sellers have a

distribution of minimal acceptable offers. The distributions have the

following mathematical representations:


(2.1) U(p) = f p(x)dx
0
where U(p) = the cumulative distribution of offers that would

accept a price not less than p,

p(x)dx = probability density function of acceptable offers.

(2.2) 1 W(p) = f w(x)dx


where 1 W(p) = the cumulative distribution of bids that would

accept a price not more than p,

w(x)dx = probability density function of maximal acceptable

bids.

The asymptotic supply and demand functions (the offer and bid cumulative

distributions respectively) have the expected first partial. That is

@U(p)/ap > 0 and aW(p)/ap < 0. The asymptotic market clearing price is

the solution to the following:









(2.3) U(p) = l-W(p)

Letting p be the market clearing price, then p is the solution to 2.3.

Further, assume there are N bids and M offers in the sample; then a

bilateral auction process yields a price PM,N. Telser's theory asserts

that as M and N become large PM,N approaches p Another result of the

theory is that the random variable PM,N P has an asymptotic normal

distribution with mean zero and variance a2 where

(2.4) a2 = 02 + 0 2
u w
(2.5) a2 = [1/P(PE)][U(p )][1-U(p )]/M

and (2.6) o2 = [1/w(p )][W(p )][-W(p )]/N.

There are two important results which follow from this theory.

First, the larger number of bids and offers the smaller is the variance.

A smaller variance implies the bids and offers are clustered tightly

around the equilibrium price. In other words the likelihood of a trans-

action taking place is enhanced. Consequently, a lower variance is

expected to increase volume or alternatively enhance liquidity. Second,

there exists an inverse relationship between the density function

parameters (i.e., w(p ) and u(p )), and the variance. Large values

of these parameters imply more homogenous bids and offers. The homogeneity

of bids and offers suggests a smaller variance.

The Benefits and Costs of Futures Markets

Telser incorporates the liquidity theory into a model of futures

market behavior. Market behavior is characterized by the level of open

interest and the level of volume. Market participants (hedgers and specu-

lators) desire to hold futures commitments. As such open interest is a

logical candidate as a variable that measures market behavior. In order










to obtain a futures commitment an exchange must be made. The transactions

that take place are important with regard to market liquidity as suggested

above. An increase in the volume level reduces the costs of transacting

which is beneficial to hedgers and speculators who desire to hold

commitments.

These ideas are summarized in the net benefit model given below:

(2.7) N = b-c

(2.8) b = b(o, Z, Eb)

(2.9) c = c(o, v, Ec)

(2.10) z = (v, ER)

where

N = net benefits,

b = benefits,

c = costs,

o = open interest

t = liquidity,

v = volume,

and Eb, Ec, E = exogenous variables.

Traders benefit from holding open interest because of the transactions

cost advantage of futures markets. Hence, the partial, ab/ao is posi-

tive. Increased liquidity raises the benefits of the market because it

reduces further the transactions cost (ab/ai > 0). Volume, especially,

and open interest as well are not costless. The cost of processing

transactions and accounting open positions is some positive function of

the volume and open interest levels, respectively. Consequently, ac/ao

and ac/av are positive. Tracing the effect of an increase in volume, it










is apparent that costs increase but benefits increase also because of

increased liquidity. The effect on net benefits depends on the marginal

conditions of 2.7.

Telser argues that the futures market will evolve if 2.7 is posi-

tive. Furthermore, markets that do evolve have activity levels that

maximize the value of the net benefit function. In other words, market

behavior is optimal in that the level of open interest and volume

maximize 2.7. The exogenous variables Eb, E and E are the commodity

specific effects which affect net benefits. As an example, consider

the uncertainty of commodity prices across markets. It is argued

throughout the literature on futures markets that price risk is a

factor which influences the use of futures markets. Participants

dealing in commodity markets with a greater degree of price risk,

however measured, benefit from the use of futures markets.


An Empirical Model

Using the conceptual net benefit model, Telser developed an econo-

metric model that sought to. explain the endogenous variables, volume

and open interest. Cross-sectional and time series data were used in

the single equation estimation of volume and open interest. Since the

cross-sectional data were pooled it was assumed that the underlying

net benefit function was of the same form across markets. Cross-sectional

differences are explained by differences in the levels of the exogenous

variables cross-sectionally. A problem with the cross-sectional

analysis of futures market behavior is the fact that contracts of

different commodities are not directly comparable. For example, the

wheat contract is expressed in terms of 5000 bushels, whereas the fresh

shell egg contracts are in 22,500 dozen units. Comparing levels of










activity on a contract basis is meaningless since the contract can be

arbitrarily defined. Accounting for this problem, Telser multiplies

the open interest and volume levels by the mean price of the commodity

calculated over the 12 year sample period. Hence, all contracts are in

common units (i.e., dollars).

Three different measures of commodity price risk were used as ex-

planatory variables in both the volume and open interest equations.

In addition theory suggested the use of a scale variable to account for

the potential use differences across commodities. It is argued that the

larger the number of potential futures market participants the greater

the benefit of a futures market. If, for instance, there were only a

few potential users the benefit of organizing a futures market to

facilitate forward dealings would be small. This follows because a

small number of potential users (i.e., the fewer the bids and offers)

implies an illiquid market which is costly on a transactional basis.

Further, the search costs involved in finding an appropriate forward

contract would not be so great if there were only a few market par-

ticipants involved in forward dealings. The level of stocks was

thought as an adequate proxy for the scale variable capturing potential

use. However, Telser argues that the stock variable would introduce

serious measurement error. Consequently, volume was used as a proxy for

the scale variable in the open interest equation while the open interest

variable was used as the proxy in the volume equation.

The empirical model Telser estimates is specified as follows:

4
(2.11) log(o) = a + a, log(v) + i2 aiX + p,

4
(2.12) log(v) = 6 + 61 log(o) + E 6iXi + 12
i=2










where o = open interest,

v = volume,

Xi = the ith measure of price variability,

1j = disturbance term j = 1, 2.

OLS estimation was implemented on 2.11 and 2.12 although Telser admits

the problem of simultaneity. The results showed that the elasticities,

a1 and 61, were positive as expected. This suggests that as volume
increases market liquidity is enhanced which reduces the transactions

costs of those who desire to hold open commitments. Consequently, more

open interest will be held. In addition, one measure of price vari-

ability was found to be positive and significant in the open interest

equation. The measure was the coefficient of variation computed from

annual prices. The positive parameter value in the open interest

equation suggests the price risk motivation to hold futures. The

coefficient of variation had a negative effect on volume suggesting that

price volatile markets are less liquid and accordingly more costly to

use. Although there is some question concerning the depth of Telser's

analysis, both from a theoretical and empirical standpoint, it provides

innovative contributions to futures market research. The presentation

of this research serves as a foundation for the analysis which follows.
















CHAPTER 3

THEORETICAL FUTURES BENEFIT MODEL


One of the major objectives of this research is to propose a theo-

retical model of futures market behavior. It is argued that the market

evolves if its benefits exceed the costs. Market behavior is expressed

in terms of hedging open interest, speculation open interest, and volume.

A net benefit model which explains the market behavior variables will be

setforth. Existing or potential futures markets are cast in a dynamic

commodity complex. The commodity complex is defined as the structural,

institutional and legal setting in which the economic activities relating

to a commodity are conducted. In fact, net benefit cannot be adequately

addressed without making this distinction. The proposed model will show

the effects of the economic environment on market behavior. At the

chapter's conclusion a summary of the theoretical results will be given

(Table 3.1). While the net benefit concept is derived from Telser's

work, the present model is a significant departure in that both hedging

and speculation are considered.


The Benefit Equation

Exchange members band together for the purpose of trading futures

contracts. More precisely, however, the members desire to hold futures

contracts; i.e., they desire to hold open interest. A futures market

transaction facilitates the holding of open interest. The benefit of

holding futures contracts is the transactions cost advantage relative

to other means of forward exchange.










Although all traders benefit from the transactions cost advantage

of futures trading, it is possible to distinguish the benefits that

accrue to the two trader-types: hedgers and speculators. Hedgers

specialize in the producing, processing, storage, exporting, etc. of

the commodity. Hedgers complement these activities with a marketing

program. Marketing activities whether they be spot exchange or forward

contracting are not costless. For instance, there are transactions costs

incurred by the buyer and seller in their attempt to negotiate a for-

ward contract. In addition to the price and quantity, aspects relating

to form, spatial and temporal dimensions must be considered in the bar-

gaining process. There may be considerable search costs involved in

finding an appropriate party to make a mutually acceptable contract. The

transactions cost saved via the holding of futures facilitates speciali-

zation in what hedgers do best, e.g., producing, processing, etc. The

futures contract serves as a temporary substitute for a merchandising

transaction.

The reduction in transactions cost obtained via futures market is

offset to some extent by the poor substitutability of the futures con-

tract. In this sense, poor substitutability means the futures contract

has attributes that do not perfectly coincide with the forward needs of

the hedger. Since the futures contract represents a highly standardized

forward agreement, it is likely that some potential users will not find

the futures contract acceptable as a forward instrument of trade. How-

ever, those who find the futures contract not acceptable, say for quality

differences, can nonetheless benefit from use of a futures contract. In

such a case commodity dealers take on an additional risk to obtain the

advantages of futures. This additional risk is commonly called basis

risk (see Chapter 2).









Speculators also benefit from the low transactions cost of futures

markets. Speculators provide time utility to commercial interests in

their forward dealings. Since the desire to forward deal by buyers and

sellers may not occur coincidentally, there will be a time gap prior

to a contract agreement. Speculators can bridge the gap by assuming the

open end of a contract expecting to profit when the positions are re-

versed. A successful speculator must be able to correctly assess

commercial needs; otherwise speculators will incur losses in providing

time utility. Futures markets facilitate this type of speculative

activity since the cost of transacting is low. Without a standardized

contract speculators bear considerable risk in forward dealings. At

one extreme a forward agreement written by a commodity dealer may

not be useable elsewhere. Hence, speculating on that agreement would be

costly. Even if it were useable elsewhere there may be substantial

search costs incurred by speculators through their efforts to provide

time utility.

Speculators provide time utility to commercial interests in their

quest for speculative profits. Profits depend on an accurate assessment

of hedging needs. Since hedging needs are a current representation of

future supply and demand conditions,the hedging role in the price forma-

tion process of futures markets is important. In markets with little

or no hedging interest there is no assurance that the futures price is

an unbiased estimate of the expected spot price. Since the plans of

commercial interests are not brought to bear on the futures price, it

is highly unlikely that a purely speculative market could price correctly

[Gray, 1967]. In a market dominated by speculators it becomes more

difficult to sort out the information pertinent to hedging needs. Con-

sequently, the risk of an inaccurate assessment of hedging needs increases.










Thus, speculation in futures markets also is confronted with a tradeoff.

Benefits accrue from the low cost of transacting yet speculation un-

bounded carries greater speculative risks.

The above arguments are summarized in the benefit equation which is

implicitly written as follows:

(3.1) B = B(H, S, L, ZB)

where

B = benefits,

H = hedging open interest,

S = speculation open interest,

L = liquidity,

ZB = an exogenous variable.

The benefits in 3.1 represent the transactions cost advantage of futures

markets. These benefits arise because futures markets are highly liquid.

Ultimately, both hedgers and speculators benefit from the trade of a

standardized contract on an organized market. Although no attempt is

made to explicitly define the benefits that accrue to the traders from

market use, it is assumed that market benefits (i.e., B in 3.1) can be

expressed as a function of hedging and speculative use. Hedging and

speculative use is defined as the level of hedging and speculative open

interest respectively. Benefits are an increasing function of hedging

and speculation but the rate of change is assumed to be diminishing
aB B3B
(i.e., T= B > 0, B < 0 i = H, S).

The appearance of the hedging and speculation variables in 3.1 is a

contrast to the Telser model in which hedging and speculation open

interest were not distinguished. It is argued that the composition of

traders in terms of their use level is a significant factor affecting










the evolutionary process of futures markets. This argument is supported

by the literature which suggests markets evolve in response to hedging

needs. Moreover, successful markets require speculative participation

[Gray, 1966]. Earlier it was suggested that futures markets could exist

without any speculative activity. Since the forward dealings of commodity

buyers and sellers may not coincide, the need for speculation immediately

arises. Consequently, the benefit equations have both hedging and specu-

lation open interest as arguments. Hedging open interest is defined as

short hedging open interest plus long hedging open interest and specula-

tion is similarly defined. It does not appear that a long or a short

commitment makes a difference as far as benefits are concerned [Ward, 1974,

p. 154]. As such, the long and short positions are combined yielding

aggregate measures of hedging and speculative participation. Furthermore,

only limited insight into the growth and dynamics of futures trading can

be gained when aggregative open interest is considered. Whereas, a number

of important policy questions, as will be seen, can be addressed when the

types of trading activities are evaluated.

A liquidity variable, L, also appears in the benefit function.

Since the degree of liquidity in a market raises or lowers transactions

costs, it will have a corresponding effect on benefits. An increase in

liquidity will raise the benefits of a market (i.e., -- = bL > 0). It
a B
is also assumed that L is negative and the liquidity of a market is

endogenously determined along with speculation and hedging. Telser [1980]

argued that the volume of trading affects the liquidity of a market and

this relationship is expressed as follows:

(3.) L = L(V, ZL)

where V = volume,

ZL = exogenous variable.










a a 2L
The partial, and -, are assumed to be positive and negative, respec-

tively. Substituting 3.2 into 3.1 yields the following:

(3.3) B = B(H, S, V, ZB, ZL)

The cross partial of H, S, and V with respect to B are assumed to be

positive.

It is assumed that B is of the same form across.markets and over

time. Benefits across markets are a result of differences in the

endogenous variables (H, S, and V) as well as differences in the exog-

enous variables (ZB and ZL). The endogenous nature of H, S, and V owes

to the fact that market evolution and propagation is determined within

the system. The futures market is an institution within the broader

commodity complex. If conditions of that complex are favorable the

market may evolve. Hence, H, S, and V may take on positive values.

The commodity complex is unique to the specific commodity. That is there

are cross-sectional differences in the commodity complex across a

spectrum of characteristics and these characteristics may influence

futures market benefits. It is assumed that these characteristics are

determined exogenously and are represented by ZB and ZL in 3.3. A

discussion of these characteristics and their effect on the model is

deferred to a later point in this chapter.


The Cost Function

The exchanges which facilitate futures trading in the U.S. are non-

profit organizations. Although exchanges provide a basis for members to

profit, making money is not an exchange objective. Some of the princi-

pal exchange objectives include: "(1) to establish equitable principles

of business conduct by members, (2) to provide an organized market place










and establish the time of trading, (3) to provide uniform rules and

standards for the conduct of trading, (4) to establish uniformity of

contract size and trade customs regarding quality and its establish-

ment, time and place of delivery, and terms of payment, (5) to collect

and disseminate price and market information to members and the public,

(6) to provide a mechanism for the adjustment of disputes among members,

and (7) to provide machinery to guarantee the settlement of contracts

and the payment of financial obligations in connection with trading

among members" [Hieronymus, 1971, p. 14]. These objectives if carried

out are not costless. Processing the trades alone is an expensive pro-

cess. To cover these costs exchanges earn income from three sources:

investments, dues, and fees. Dues are paid by the exchange membership.

Membership is limited by the organizational charter in many cases. Ob-

taining membership via the purchase of a seat on an exchange is not

costless either. The value of a seat is market determined.

Exchange costs can be expected to rise with use. Assuming that a

single futures market constitutes an exchange, the futures market cost

function is implicitly written as follows:

(3.4) C = C(H, S, V, ZC)

where C = costs,

ZC = exogenous variable.

The following assumptions are made regarding the first and second partial

of the endogenous variables: HC = CH > 0; CS > O; CV > 0; CHH > 0,

CSS > 0; CVV > 0, CHS > 0; CHV > 0; and CSV > 0. The exogenous influence

given by ZC may vary cross-sectionally or intertemporally. A detailed

discussion on this exogenous variable will also be taken up later in this

chapter.










The Net Benefit Function

Subtracting 3.4 from 3.3 yields the following implicitly defined

net benefit function:

(3.5) N = N(H, S, V, Z)

where N = net benefits,

Z = exogenous variable(s).

For pedagogical purposes only one exogenous variable (Z) will be initial-

ly considered. Whereas, in the final model a number of exogenous effects

must be incorporated into the analysis. The general results of the

trading model can be illustrated with only one exogenous variable with-

out any loss to the final results. Introducing all of the exogenous

variables at this time is unnecessary and complicates the analysis.

It is assumed that futures market behavior expressed in terms of

hedging, speculation, and volume is optimal if that behavior maximizes

the net benefit function. Furthermore, it is assumed that futures

market behavior is optimizing. It has been suggested that the optimiz-

ing criteria could be the level of activity which maximizes the value

of the membership [Higinbotham, 1976]. However, the value of a seat

is insignificant relative to the value of the business conducted by

most members. Assuming that the net benefit function is well behaved

(i.e., strict concavity) the optimal values of the endogenous variables

can be easily derived. Taking the partial of N with respect to the

endogenous variables and setting them equal to zero yields the set of

first order conditions:

(3.6) NH = NS = NV = 0
aN
where N = and i = H, S, V.
i ait

Assuming that the second order conditions for a maximum hold, the optimal










values of the endogenous variables can be obtained by solving 3.6 simul-

taneously. The solution, using asterisks to denote optimal values, is

written as follows:

(3.7) H* = H*(Z)

(3.8) S* = S*(Z)

(3.9) V* = V*(Z)
If 3.5 is strictly concave, as assumed, the following second order

conditions are implied:

(3.10) NHH < 0, NSS < 0, and NVV < 0.

(3.11) NHHNSS NSHNHS > 0, NHHNVV NVHNHV > 0, and

NSSNVV NVSNSV > 0.

(3.12) HH NHS HV
DET NSH NSS NSV < 0.

NVH NVS NVV


Exogenous Effects on Trading

The exogenous variable, Z, may change which would affect the
optimal solution (3.7 3.9). To ascertain the impact of such a

change a comparative static analysis is presented. The total deriv-

ative of 3.6 with respect to Z gives the following:
SdH
(3.13) HH NHS NHV NHZ

dS
NM N N dS N
SH NSS NSV dZ SZ
dV
N N N V N
NVH VS VV dZ _VZ

The effect of Z on each endogenous trading variable is then easily solved
as shown in (3.14).









(3.14) d- N N N
dZ NHH NHS NHV HZ
dS NSH NSH Nsv NSZ
dZ
dV NVH NVS NVV LNVZ
dZ

If NHS = NSH = NHV = NVH = NSV = NVS = 0, equation 3.14 simplifies to
3.15:
dH -N )- 0 0 N
(3.15) dZ HH HZ

dS 0 (Nss)1 0 SZ

dV 0 0 (NVv )- Nv
dZ -

Since the diagonal elements of the inverse matrix are negative the signs
dH dS and dV
of the derivatives (dZ and dV) in 3.15 will depend on NHZ, NSZ, and

NVZ, respectively. If the off-diagonal elements of the matrix in 3.14
dH dS dV
are not equal to zero, the signs of derivatives (d-' and d) are not
readily determined. Given that the cross partial of the endogenous
variables (i.e., NHS, NHV, NSV, etc.) are not zero, the derivatives have
the following form:
(3.16) dH = N-N
(3.16) = (-NHZ (A1) + NSZ (A2) + NVZ (A3))/D

(3.17) Z= (NHZ (A2) NSZ (A4) + NVZ (A))/D
dV = (A ))/D
(3.18) = (NHZ (A3) + NSZ (A5) + NVZ (A6))/

where A = NSSNVV NVSNSV'

A2 = NHSNVV NVSNHV'

A3 = NHVNSS NHSNSV

A4 = NHHNVV NVHNHV'









A5 = NSVNHH NSHNHV'

A6 = NHHNSS NSHNHS'

D = NHH (A) NSH (A2) + NVH (A3).

The denominator in 3.16, 3.17, and 3.18 is the determinant of the

symmetric matrix in 3.13 which must be negative for the second order

conditions to hold. Thus the signs of the above derivatives depend on

the expressions in their respective numerators. However, unless one

is willing to make assumptions regarding the signs of the cross-partials,

namely NHS, NHV, NSV, NHZ, NSZ, and NVZ, the signs of the derivatives

must be empirically determined.

The derivatives from 3.16, 3.17, and 3.18 can be decomposed into

two important effects. The partial effect is the result given by 3.15

when the cross-partials, NHS, NHV, and NSV, are assumed to be zero. It

measures the change in the endogenous variable given a change in Z,

ceteris paribus. On the other hand, the total effect measures the

change in the endogenous variable as a result of a change in Z and a

change in the other endogenous variables caused by the change in Z.

In the figure below the partial effect of Z is illustrated (Figure 3.1).

A change in Z from Z to Z shifts the NH function outward. The partial

effect of Z on the hedging first order condition is given by the verti-

cal distance, oa. If the cross-partials of the endogenous variables are

independent then the total effect and the partial effect are equivalent.

In this case optimal H increases from H to H1 as a result of the change

in Z.

The theoretical results obtained are very useful with regard to

empirical applications. Consider the econometric estimation of the three











































Figure 3.1--The effect given a change in Z










equation system given by 3.6. The structural parameters of this

econometric problem correspond to the partial effects of the exogenous

variable on the endogenous variables. On the other hand, the total

effect is analogous to the reduced form parameters of this econometric

system. In the empirical analysis of this study, this econometric

analysis will in fact be conducted. An empirical approximation to 3.6

will be subjected to parameter estimation using an appropriate econo-

metric technique. An objective of this research is to obtain empirical

information regarding the structural parameters of the proposed system.

Since the structural parameters correspond to the partial effects of Z

on the endogenous variable, testable hypotheses can be made if the signs

of the partial effects are known. From 3.15 it is apparent that the

signs of the partial effects depend on NHZ, NSZ, and NVZ, respectively.

Knowledge of these cross-partials will depend on the specific exogenous

variable in question. In the next section the exogenous variables of the

model will be introduced and the discussion will center on the effect

of the exogenous variable on NH, NS, and Nv.


The Exogenous Influences


Futures market behavior has been characterized by the net benefit

function. Net benefits were argued to be some function of hedging,

speculation, and volume. Since the net benefit function may not be

stable over time as well as across markets, an exogenous variable Z was

introduced to capture the effects of the cross-sectional and intertemporal

influences. In this section these influences will be identified and the

partial effects of the exogenous variables will be hypothesized.










Up to this point the exogenous effects have been discussed mathe-

matically using Z as a representative exogenous variable. A major con-

clusion was that the sign of the partial effect was equivalent to the

sign of NiZ (i = H, S, V). The sign of NiZ in turn depends on the manner

in which Z interacts with the endogenous variables within the context

of the net benefit function. The exogenous variables do not give rise

to net benefits independently of the endogenous variables. However,

the exogenous variables can affect net benefits through their impact

on hedging, speculation, and volume. As such the analysis of the partial

effects will concern itself with this interaction. In the subsequent

discussion, exogenous variables that directly influence the trading

activities are setforth.


Market Power

There is increasing evidence that market power is present in agri-

cultural commodity subsectors [U.S.D.A., 1972]. Some of these commodities

are traded on futures markets. Market power and futures trading are

mutually exclusive processes [Paul and Wesson, 1960). That is, market

power and futures trading are not compatible suggesting futures trading

benefits decline where market power exists. To investigate this further

consider a commodity that is sold by industry A and bought by industry

B through spot or forward transactions. In addition, assume the exis-

tence of a futures market as an alternative means of forward exchange.

In the event that industry B is a single firm (i.e., a mondposonist)

there are no benefits in having a futures market. Since all the firms

of industry A sell strictly to the monopsonist there are no search costs

involved as far as forward dealings are concerned. Moreover, the timing










of the transaction between the sellers of industry A and the monopsonist

is undoubtedly dictated by the monopsonist. Recall that the futures

market facilitates the separation of ownership and control of the

commodity in an illusory sense. Since the sellers have no other market

outlet, the effect of the monopsonist is analogous to the futures market's

role in the separation of commodity ownership from its control. In

effect the monopsonist owns the commodity and extracts from the firms of

industry A a monopsonistic rent for its control.

The presence of a futures market where there is a single commodity

buyer also raises questions concerning the price discovery process of a

futures market. A monopsonist would pose a serious threat to the

possibility of a squeeze or other manipulative practices. If a market

was characterized by this sort of activity short hedgers would suffer

from high hedging costs because of the imminent possibility of price

bias. In addition large transactions by the monopsonist may cause

distortions in futures prices if there is not sufficient speculation to

absorb such activity. There is also the argument that the monopsonists

would prevent the development of a market because of fear that their

market power would be threatened. In any case, the presence of a

monopsonist buying in a market that supports a futures market seems

highly unlikely. The same holds true for the case of a monopolist

facing many buyers. However, this conclusion does not necessarily

hold for intermediate cases of noncompetitive markets as evidenced by

existence of futures markets in commodity markets having noncompetitive

elements.

An imperfectly competitive market is a market where one of more

buyers or sellers have a "perceptible influence" on price. This









definition is quite broad as it encompasses many different types of

markets. However, most imperfect competition models are characterized

by a limited number of buyers or sellers. Market power or the evidence

of a noncompetitive market may also have implications regarding firm

size. There may be many buyers yet the existence of one large buyer

is sufficient for the presence of market power. The degree of product

differentiability is also a criteria that distinguishes market compe-

tition.

Firm size is expected to have a positive effect on the net benefit

function through its effect on hedging. Larger firms may relap economies

in their futures trading activities. Large firms may be able to employ

a specialist to handle the hedging needs of the firm. In addition large

firms may have access to better information which can enhance hedging

activities. On the other hand small firms may not be able to place an

appropriate hedge because the contract is too large relative to firm

size. Consequently small firms may not hedge at all.

In the monopsonistic case it was argued that the benefit of a

futures market was dubious because price is set. For similar reasons

it follows that in intermediate cases the benefit of trade in a

standardized forward contract is lessened. Firms with market power

would not encourage the development of an additional market outlet.

Furthermore, potential manipulative effects of large firms on futures

prices could deter potential hedging activity as hedging costs become

enlarged. Thus, in markets that are susceptible to price effects by

the actions of one or more firms the benefits of a futures market are

lessened relative to an idealized case where price effects do not occur.










For most markets, the concept of market power lies in the continium

from perfect competition to pure monopoly. The theory is quite clear for

the extremes but less so from the range of structures lying between these

extremes. While the exact quantification of these structures is at best

approximate, one can argue that the usefulness of futures declines rapidly

as the structure approaches the extreme point of monopolistic power.

Whether the decline is continuous throughout is debatable. Use of for-

ward markets may require some minimal level of size (e.g. management,

capital, etc.) and highly atomistic structures may in fact not yield the

highest level of trading. Some trading may increase with size due to

economies of scale but eventually the problems resulting from increased

market power must lead to an economic environment not condusive to futures

trading.

The number of firms affects market benefits in another way other than

its effect on the competitive price discovery process. In the monopsonis-

tic case the exchange arrangements are greatly simplified as all sellers

deal with a single buyer. An increase in the number of buyers increases

the number of possible exchange arrangements. A large number of buyers

and sellers having forward dealings would benefit from trade in a

standardized contract on a highly liquid futures market. This follows

because the search costs involved in finding an appropriate forward deal

can be expected to increase as the number of exchange arrangements in-

crease. Conversely, the benefit of a futures market when industry A and

industry B have a small number of firms is not great because search costs

are not great. Thus the number of firms should have a positive effect

on net benefits through its interaction with the hedging variable.










Price and Basis Risk

Price risk is often cited as being a motivating factor behind the

decision to enter a forward arrangement. Commodity handlers would pre-

fer to produce, process, store, or transport the commodity rather than

assume the risks of ownership. In this context risk is taken to be the

uncertainty regarding the expected spot price. A rigorous treatment of

risk in light of the persistent attacks will not be attempted [Gray and

Rutledge, 1971]. However, the empirical measure of risk which will be

presented in a subsequent chapter is consistent with applied research.

Thus the only weakness is that the vagueness problem usually associated

with the definition of risk is not clarified by this research. Never-

theless, it is argued that the benefits of a futures market are greater

in markets that experience greater price risk. Forward contracts, once

agreed upon, require the two parties to fulfill the contractual obli-

gations including delivery. If a subsequent price drop occurs during

the contract period and a further drop is expected, the buyer is placed

in an unenviable position. Futures contracts are highly standardized

and perfectly substitutable. As such these contracts can be exchanged

to obtain a more favorable marketing position. When commodity prices

are highly volatile a more flexible position is preferred. There is

an implied transactions cost associated with being bound by a forward

contract at least for one party to the contractual agreement. This cost

is assumed to be greater in markets with greater price risk. Hence, the

price risk increases the marginal effect of hedging on net benefits (i.e.,

NHR > 0, where R = the degree of price risk).

Price risk is also expected to affect net benefits through its inter-

action with speculation. In the event futures trading was nonexistent

speculation on commodity prices would probably still be evident.










Speculators could engage in such activity on a forward contract basis or

outright ownership. In a market typified by price volatility there is

an additional cost speculators incur. Like the case of the hedger, it

might not be easy or costless to effect an immediate transaction to enter

a more favorable marketing position. In highly liquid futures markets

the cost of reversing an unwanted position is nominal. Thus, it is

argued that price risks have a positive effect on the incremental effect

of speculation on net benefits. Futures speculation primarily arises

from movement of capital among alternative speculative financial instru-

ments. Rising price variability implies greater risk but likewise

greater opportunity for windfall gains. Hence, this rising opportunity

should attract speculative capital in a market that otherwise has a

small cost of entry and exit.

Basis risk is an important concept with regard to hedging on futures

markets. In general hedgers substitute price risk for basis risk as a

consequence of their hedging activities. In the ideal situation the

basis tends toward zero in the delivery month of a future. If a short

hedger entered a hedged position when the basis is positive and closed

out that position in the delivery month when the basis is zero the

hedger would have earned a positive return on the hedged commodity [Ward

and Niles, 1975]. However, the basis need not be zero during the de-

livery month. Since there is a positive cost of delivering (or taking

delivery) on a futures contract, there is a range of basis values in

which the cash and futures prices may move independently of one another.

Consequently, there is some risk that losses may result from an unfavorable

basis when the futures position is closed. As a general rule a commodity

dealer reduces the exposure to risk on a hedged commodity unit if the










basis risk is less than the cash price risk. An increase in the basis

risk reduces the effectiveness of futures market with regard to risk

reduction.

The partial effect of a change in basis risk is illustrated below

in Figure 3.2. Assuming that the total effect and the partial effect

are equivalent,an increase in basis risk decreases optimal H from HO and

H1. Recall that the partial effect and the total effect are equivalent

if the endogenous variables are independent of one another (i.e., NHS
dH
NHV = NSV = 0). Thus, the partial effect is given by 3.15 (i.e., dBR
-NHBR
NHBR, where BR is defined to be a theoretical measure of basis risk).
NHH
Since NHH is assumed to be negative, an increase in BR reduces H. This

suggests that futures markets with greater basis risk discourage hedging

activities, ceteris paribus.

Maturation

Newly developed markets often pass through a phase of relative in-

activity. As time passes trading increases significantly as the market

develops its hedging and speculation business. Working [1953] suggested

that in newly developed markets it is expensive to hedge because of

liquidity problems. Sufficient speculation is required to provide the

necessary liquidity to attract the potential hedging use of a futures

market. It has been said that floor traders and scalpers may have to

sustain losses in the start-up period to ensure the necessary level of

liquidity [Working, 1962]. In other words speculators would have to

make trades at prices that would otherwise not be made for the purpose

of getting a market off the ground.

In this so-called start-up period of a market, there is likely to

be significant information problems. These problems could result from

















BR




BR1 BR
BR













0 H1 HO







Figure 3.2--The partial effect of an increase in basis
risk (BRu to BR1)










the lack of understanding regarding the hedging use of the futures mar-

ket. It may take years before the commercial interests understand the

mechanics of using futures market to its fullest potential. Dissemina-

tion of information by the exchanges, government, academic institutions

and other private concerns associated with the commodity trade tends to

alleviate this problem of market development. Consequently, one can

expect an increase in hedging activity as the knowledge of the potential

participants, including speculators, increases. The effect of maturation

on the partial of net benefits with respect to H is expected to be posi-

tive. The same holds true for maturations effect on the partial with

respect to S. However, if there is a large amount of speculation initially

because of the markets "newness" or because of a sacrifice to ensure

market development and continued success, then the affect of maturation

on the partial of S should be smaller than for hedging. For instance,

if the "newness" attraction wears off then speculation would be expected

to decline in response to maturation. Thus the effect of maturation on

speculation may be an empirical question.


Perishability and Spreading

Commodity perishability was long thought to be an impediment for

a commodity to be traded on a futures market. History has proven this

wrong with the successful nonstorable commodity futures markets such

as fresh eggs and live cattle. Nonetheless, the degree of perishability

can affect futures market behavior in several ways. Commodities that

are storable can be hedged several times before the commodity is con-

sumed. For example, producers of wheat could short hedge expected pro-

duction. Upon harvest stockholders build-up inventories from the fall

harvest and subsequently short hedge these stocks. Hence, the same










commodity unit could be hedged twice. Storability gives rise to addi-

tional economic functions (i.e., storage) which benefit from the

presence of futures markets. Commodity futures trading in a highly

storable commodity should benefit from the positive effect of stor-

ability on hedging use.

The degree of perishability can also affect the speculative trade

on futures markets. The different futures contracts for a storable

commodity are very similar with regard to their respective prices.

Theoretically, the current price difference between any two futures

is equivalent to the per unit storage cost of carrying the commodity

over the time period that separates the two futures. Thus a change in

one price will bring about a change in the other given a constant storage

cost. In these markets speculators may opt to enter a spread position

across futures of differing maturities. Since prices tend to move to-

gether a gain in one future is usually matched by a loss in the other.

Speculative profits would depend on the movement of price differences

while the spread was intact. Although the expected return on a spread

position may be less, the risk of a spread position is also less because

of the high correlation in the futures prices in storable commodity

markets. Exchanges encourage spreading activity by lowering commission

charges and margin requirements on spread positions. Hence, spreading

activities increase speculative opportunities.

In nonstorable commodity futures markets futures of different

maturities are essentially different commodities. The price of eggs for

delivery in two months and the price of eggs for delivery in four months

can reflect two fundamentally different sets of expected supply and

demand information. Since eggs cannot be stored the price differences










between the various futures have no theoretical interpretation. Con-

sequently, spreading in such a market may be risk increasing. As such

it is argued that storability enhances speculative use of futures markets

(e.g., NSPE > 0, where PE = the degree of perishability) because of the

increased speculative opportunities. It could be argued, however, that

there is a given stock of speculative dollars in a given futures market

and that spreading does not encourage additional speculation to enter

the market. Rather spreading opportunities may shift that stock of

speculative dollars from open positions to less risk spread positions.

If this effect prevailed aggregate speculative use of futures markets

would not be affected by commodity perishability.

Spreading also occurs across markets. Many commodities have prices

that are highly correlated with one another because of a spatial, form,

or substitute relationship. Price spreads between these commodities tend

to be more predictable than the commodity prices. Thus speculating on

price spreads across markets is less risky although the expected return

may be lower as well. Lower commission charges and margin requirements

are also given for intermarket spreading. Since intermarket spreading

increases speculative opportunities it would seem that intermarket

spreading would increase the incremental effect of speculation on net

benefits. However, it is possible that intermarket spreading could

siphon off speculative capital that would otherwise be spent on open

positions in one market. This possibility is based on the assumption

that there is a stock of speculative capital that seeks profitable

alternatives. Consequently, intermarket spreading alternatives could

reduce the speculative use of a given market. The effect of intermarket

spreading in this case reduces the marginal effect of speculation. Thus










the partial effect of spreading on speculation is nebulous, suggesting

that the effect of spreading is an empirical question.

Perishability and intermarket spreading have a similar effect on

market liquidity. Recall that storable commodities have futures prices

that are related intertemporally by a storage cost function. There may be

intramarket spreading opportunities if the price spreads do not reflect

storage costs. In other words arbitrage between the futures of different

maturities would ensue. Spreading whether it be intramarket or inter-

market improves the price discovery process because it increases the amount

of information focused on that process. In Telser's model of the theory

of market clearing prices it was observed that the homogeneity of the bid

and ask prices reduced the variance of market clearing prices or alterna-

tively increased liquidity. Prices discovered in markets with many spread-

ing opportunities are expected to have more homogeneity of bid and ask

prices because of the information regarding the price spreads. Thus a

storable commodity futures market with many intermarket spreading opportuni-

ties increases the marginal effect of volume on net benefits. Storability

and the presence of intermarket spreading opportunities are reinforcing

effects.


Speculative Opportunity Cost

Speculative dollars can find other investment opportunities aside

from the holding of futures contracts. Government securities offer rates

of return that are virtually risk free. The New York stock and bond mar-

kets also compete for speculative dollars. It is argued that speculative

dollars in futures markets are sensitive to alternative rates of return.

As rates of return on alternative modes of investment increase specula-

tive dollars will be drawn from futures markets, ceteris paribus. Thus,










rates of return on alternative investments will decrease the marginal

effect of speculation on futures market net benefits.

Effect of Government Price Supports

A number of commodities that are traded on futures markets are

included in the U.S. price support programs. Prices are generally

supported by the nonrecourse loan program administered by the government.

In the early and mid 1960's grain prices were relatively low. In some

cases the prices were at or near the support level. It has been suggested

that price support activities eliminate the need for hedging because the

government assumes the risk of commodity ownership. As cash grain prices

move towards the support level the risk that prices would fall much further

is reduced because of the price support buffer. In this situation the

need to short hedge to protect against a decline in spot prices is re-

duced. Thus, price support activities should have a negative partial

effect with respect to hedging.

Cost Effects

The exogenous variables discussed thus far have entered the model

through the benefit function or the liquidity function. There are several

readily apparent exogenous variables which cause cross-sectional and

intertemporal differences in the cost function. In the last two decades

technological improvements in communications and microtechnology have im-

proved the efficiency of processing trades and disseminating information.

The effect of technological improvement is to increase net benefits

through its positive partial effect with respect to volume. Volume is

the limiting activity in a cost sense because the number of transactions

that occur per time unit on one pit is physically constraining. The

effect of technological improvement should affect net benefits through

the volume variable rather than hedging or speculation.










There are 11 regulated exchanges in the U.S. Cost differences

across markets might arise as a result of exchange size expressed in

terms of volume. Larger exchanges that offer a single futures market

may give rise to scale economies which would lower unit costs of market

operation. Some exchanges offer trading in several futures markets.

In these cases fixed costs can be spread over more markets lowering the

unit costs relative to single market exchanges. These scale effects

raise the incremental effect of volume on net benefits.


Summary

In the previous section the major exogenous influences were identi-

fied. This list of variables is by no means exhaustive. However, the

model does not purport to explain all of the cross-sectional and inter-

temporal variability of hedging, speculation, and volume. The problem

of this "unexplained" variability will be addressed more fully with the

presentation of the stochastic model in chapter 5. Nonetheless, the

partial effects of the identified exogenous variables were hypothesized.

Recall that these effects are equivalent to the structural parameters of

the econometric model that will be estimated. These effects serve as

a basis for hypothesis testing. A major objective is to test the

hypotheses regarding the conceptual model given by 3.6. The model repre-

sents the first order conditions of the net benefit function which is a

mapping from futures market activity in terms of hedging, speculation

and volume to net benefits. Since the futures market is cast in a dy-

namic environment, the parameters of the net benefit function are expected

to adjust. The exogenous variables that have been introduced explain

this adjustment. This adjustment is manifest in the first order conditions










and its effect on net benefits is given by the partial effects dis-


cussed above.


The signs of the partial effects are summarized in Table


3.1.


Table 3.1--The hypothesized partial effects


i NHi NSi NVi

Firm Size (FS) >0 =0 =0
Price Effects of Imperfect Competition (IC) <0 =0 =0
Number of Firms (NF) >0 =0 =0
Price Risk (PR) >0 >0 =0
Basis Risk (BR) <0 =0 =0
Maturation (MT) >0 ? =0
Perishability (DP) ? ? >0
Intermarket Spreading (IS) 0 ? >0
Opportunity Cost of Speculation (OC) 0 <0 =0
Price Supports (GE) <0 =0 =0
Technological Improvement (TC) =0 =0 >0
Economies of Scale (SE) =0 =0 >0
















CHAPTER 4

A MATHEMATICAL FUTURES TRADING MODEL


The purpose of the present chapter is to operationalize the con-

ceptual model describing futures market behavior. As noted earlier

one objective was to obtain empirical estimates of the parameters of

the system given by 3.6. In this chapter a mathematical model of this

system will be developed to facilitate the econometric analysis which

follows in the subsequent chapter. In the first section of this chapter

the variables of the conceptual model will be given an empirical defi-

nition. The gaps that exist between the empirically defined variables

and their theoretical counterparts will be highlighted. In the next

section, the functional form of the equations will be addressed. Dis-

cussion will center on the use of a logistic functional form and the

corresponding implications with regard to its properties. This chapter

will conclude with a presentation of the mathematical model.

Before proceeding with the model development there are several

issues regarding the cross-sectional and intertemporal nature of the

study that will be addressed. The time framework of the present analysis

is the long run. The proposed model is attempting to explain insti-

tutional behavior. Moreover, the model seeks to explain structural in-

fluences as they relate to institutional behavior of the futures markets.

Both concepts, institutional and structural change are long run concepts;

whereas short run models regard the institutional and structural setting

as fixed.










Long run models typically utilize annual data. It will be assumed

for this analysis that a year's time is sufficient for institutional

change. There are 18 years of data available for the current research.

The range of structural effects on futures market behavior is limited

in a study of a single commodity futures market since structural change

does not occur rapidly. As such, annual data on 16 cross-sections of

futures markets will be used to capture the temporal effects that are

constrained by the number of observations in the time series. The

cross-sectional observations also provide commodity specific information

aside from the structural variation. Some of these effects which are

independent of time are the commodity characteristics such as perish-

ability. These characteristics are developed in the next section.


Variable Definitions

Hedging, Speculation and Volume

Within the context of the conceptual model hedging was defined as

the total long and short open interest held by hedgers at one point in

time. Speculation was similarly defined. Commitment data, which

classifies the open interest as either hedging, speculation, or non-

reporting are published on a semi-monthly basis [CFTC, 1978]. There are

two important problems rising with the use of thesedata. First there

are 100 or so observations on hedging and speculative open interest

within a year's period. Choosing any one of the semi-monthly observation

points as the annual observation of hedging and speculation may intro-

duce seasonal hedging or speculation patterns into the model. Averaging

the commitment data over the observation points would reduce the problem

of seasonality. As such, the hedging and speculation open interest










variables are constructed by averaging monthly commitment data using

the monthend positions.

A more serious problem with the commitment data is the fact that

some of the open positions are not classified as hedging or speculative;

rather these positions are classified as nonreporting. This occurs be-

cause regulations require that only large traders report their positions

as being hedging or speculative. The size of the position which re-

quires reporting varies from market to market. Nonetheless, the non-

reporting proportion of open interest is often significant across all

markets. Excluding the nonreporting open interest from the model would

undermine the objective of explaining futures market behavior, especially

when nonreporting data exceed the hedging and speculation. However,

this approach is a problem too because any allocation scheme will likely

be biased.

There are a few published market surveys that have classified the

open positions as either hedging or speculative and some efforts to

estimate all trader commitments using the data from these market surveys

have been completed. See Rutledge [1977], Larson [1961] and Peck [1975].

In most studies the nonreporting data are generally treated as speculative

because it is believed that there are a significant number of small

speculators present in futures markets. It has been argued that there

may be a tendency on the part of some speculators to have small positions

to avoid reporting [Rutledge, 1977]. Hedgers, on the other hand, are

less inclined to worry about the reporting and more inclined to place a

hedge that optimizes some marketing strategy.

Two allocation schemes for dealing with the nonreported data will

be utilized in this study. One allocation scheme allocates the nonreporting










data between hedging and speculation based on the proportion of the

reported data that is hedging and speculation. That is the proportion

of the nonreported data that is hedging is assumed to be equivalent to

the proportion of the reported data that is hedging (Scheme 1). The


second allocate

for the proporti

2). Consider ti

(4.1) HI


(4.2)


H2


(4.3) S2


on procedure weights Scheme 1 by a factor which accounts

ion of total open interest that is nonreporting (Scheme

ie following:

RH RH
= RH + ( )NR; S1 = RS + (1 )NR
RH NR
= RH + NR(TR)(1 NR)

NR
SRH(1 + TR + NR

= RS + NR((1 -(RH) (1- NR ))
TR TR + NR
RS + NR
= RS + NR(S + NR
TR + NR


where TR = total reported open interest,

NR = nonreporting open interest,

HI hedging open interest (Scheme 1),

S1 = speculation open interest (Scheme 1),

RH = reported hedging open interest,

RS = reported speculation open interest,

H2 = hedging open interest (Scheme 2),

S2 = speculation open interest (Scheme 2).

Under Scheme 1 allocation is based on the reported hedging and specula-

tion levels relative to total reported open interest. Whereas, under

Scheme 2 the allocation of the nonreported data is weighted in that a

greater proportion is allocated to speculation. Scheme 2 follows from

the assumption that as the proportion of nonreporting data increases, a










greater share of this increase arises from speculative activity. More-

over, if the assumption of Scheme 2 were valid, Scheme 1 would be biased

in that too much would be allocated to hedging. In either case, however,

there is no clear benchmark for evaluating the degree of bias arising from

either scheme.

These methods of allocation are much more realistic than the all or

nothing allocation to speculation or hedging. Since the nonreporting to

total open interest ratio and the reported hedging to reported specula-

tion ratio vary from market to market as well as over time, the above

methods of allocation are unique to each market. In other words the pro-

portion of hedging in the nonreporting data can vary across markets as

well as over time. This is not the case if the proportionality factor

is fixed as in the case of an all or nothing scheme or some intermediate

fixed proportionality factor. Although any allocation scheme will likely

be in error, a scheme that is more flexible is preferred. Hence, the

allocation methods outlined above will be used in the empirical analysis

that is forthcoming.

Volume is less troublesome with regard to operationalizing the

variable for empirical use. Recall that volume measures the amount of

contracts traded per unit time. Volume data are available on a daily

basis and probably could be obtained on an intra-day basis as well.

The volume variable is capturing the effects of market liquidity as an

increase in volume enhances liquidity. Given that hedging and specula-

tion are averaged daily statistics, the relevant volume statistic should

also be a daily measure. This follows in that hedging and speculative

activity respond to liquid market conditions which can conceivably change

on a day to day basis. As such, the volume variable will be constructed










by dividing the number of trading days into total volume observed in a

market for a given year. The number of trading days in a year's time

is assumed to be 250.

Another problem arises because of the cross-sectional nature of the

study. Specifically, the contracts across the agricultural commodity

markets studied differ with respect to the unit of measure. For in-

stance, the fresh egg futures contract calls for delivery of 22,500

dozen eggs, whereas the wheat futures contract traded at the CBOT calls

for delivery of 5000 bushels. A problem arises because the trading

activity in terms of contracts may not be directly comparable. Since

the contract can be changed with respect to the deliverable amount,

one can affect a change in market activity by arbitrarily changing the

quantity parameter of a contract. There is some question as to whether

contracts can be arbitrarily changed, however. Since the contracts

are drafted to meet the needs of its users (i.e., hedgers and specula-

tors), the size of the contract cannot be considered arbitrary. More-

over, contract size may also influence the liquidity and cost of a

market. For instance, if the wheat contract called for delivery of one

bushel of wheat,hedging would increase 5000 fold to meet current hedging

needs. It is questionable that a market could physically handle the

implied increase in futures market use. Thus if one accepts the propo-

sition that contract size is not arbitrarily determined then it appears

that contracts of commodities measured in different units could be com-

pared.

The above debate on contract comparability will not be resolved by

this research. A method will be adopted that allows comparability across

markets. One possible method is to express the contracts in value terms.










In other words multiply the commodity units of the contract by the

commodity price which would yield the contract value [Telser, 1980].

This approach would be valid if the analysis was a cross-sectional

study of futures markets. However, this study includes a time series

component which implies commodity price variability. The measured

changes in hedging, speculation, or volume may result from price changes

over time rather than real changes in futures market activity. Express-

ing the endogenous variables in value terms introduces price effects

which makes the analysis of real futures market activity more difficult.

An approach that avoids the problems associated with value compar-

ability is to express hedging, speculation, and volume as a percent of

potential futures market use. In this manner the endogenous variables

would be pure numbers expressed in percentages and could be comparable

across markets. Potential use is argued to be relatively stable over

time. Consequently, changes in the percentages reflect changes in

actual use which is the behavior that is to be explained. Unfortunately,

potential use, which is defined to be the maximum number of contracts

that could be held on the average by hedgers, is not precisely measurable.

A proxy for potential use is the total commodity supply observed in a

given year. The total commodity supply should bear a close relationship

to the quantity that is potentially hedgeable. Not all of the commodity

supply is hedgeable because producing firms may be too small; the commod-

ity is forward contracted; or for other reasons. However, it is assumed

that the potential hedging proportion of total supply is constant for a

market across time.

Markets are significantly different with regard to potential use.

Actual use of futures markets by hedgers in a market that has ten times










the potential use of a smaller market is expected to be larger than the

actual use of the smaller market. This follows because established

markets should have similar use patterns relative to some potential.

Thus, in a cross-sectional study of markets with significant differences

in potential use there will be scaling problems. The virtue of express-

ing hedging, speculation, and volume as a percent of the potential use

follows since the percentage eliminates the problem of contract com-

parability and the scale effect problem is circumvented.


Concentration and Firm Numbers

In the theory chapter it was argued that firms having market power

could eventually be detrimental to futures market activity. In addition,

firm size was expected to have a positive influence on hedging activities

because larger firms could specialize in futures trading activities. A

commonly used variable in empirical research that is used as a proxy for

the aforementioned structural characteristics is concentration. Highly

concentrated industries indicate the presence of noncompetitive elements.

There are several ways concentration can be measured. The most common

measure is the proportion of the industry's value of shipments accounted

for by the largest 4, 8, 12, or 20 firms. This measure ranges between 0

and 1.0 with the largest values representing the more concentrated in-

dustries. Concentration data are published once every five years by the

Bureau of Census of Manufacturers. The data are classified according to

a 4 or 5-digit Standard Industrial Classification (SIC) code. A given

4 or 5-digit code refers to industry grouping with the fewer digit codes

having more aggregative data [USDC, 1972].

Using concentration as a proxy for the effect of firm size and the

price effects of imperfect competition is not without its difficulties.










One difficulty is the fact that larger firms and the price effects of

imperfect competition imply a highly concentrated industry. However, the

effects of these market power characteristics on hedging activity are

diametrically opposed. Recall that NHIC < 0 and NHFS > 0. It is possi-

ble to reconcile these opposing effects by hypothesizing a quadratic

relationship between hedging and the level of concentration. At low

concentration levels the price effect of firm activities is perhaps

negligible. On the other hand, firms may become large enough in low

concentrated industries to reap scale economies in the use of futures

as a hedging vehicle. Hence, concentration would have a positive effect

on hedging activities. As the level of concentration increases, firm

size increases as well. In addition, the price effects of imperfect

competition may have greater presence, but concentration would have a

positive effect on hedging if the firm size effect dominated the price

effect. At higher concentration levels the price effects of imperfect

competition increase hedging costs off-setting the positive firm size

effect. Hence, at high concentration ratios the effect on hedging is

strictly negative.

In the theory chapter a model of a market was developed to facili-

tate the discussion on market power. In that commodity market model

industry A sold to industry B via spot or forward exchange which in-

cluded futures trading as an alternative. The hedgers in that commodity

market model were the firms of industry A (short hedgers) and the firms

of industry B (long hedgers). One could use the concentration ratios

of industry A and industry B to investigate the effect on the long and

short hedging activities. However, this model is at a fairly high level

of abstraction. Some commodities have more than one use. Consequently,










there may be several industries acting as buyers in the cash and futures

markets. The storage function for storable commodities is complimented

by short hedging activities which is also the hedging strategy employed

by commodity producers. Since commodity users may stockpile their

commodity needs and place short hedges, the number of potential commodity

sellers is far greater than the number of producers. Hence, there may

be a large number of industries using any one futures market. This

makes the analysis of the effects of concentration more difficult.

In this study a simple approach to ascertaining the concentration

effects will be adopted. Rather than include all the concentration in-

formation regarding the industries that use futures markets in the model,

only one piece of concentration information will be used. The piece of

information used is not arbitrarily chosen, however. The effects of

concentration on hedging will not be pronounced if a concentrated in-

dustry makes minimal use of the futures market. Moreover, the concen-

tration ratios for many agricultural commodity producers are very small,

probably approaching zero in many cases. Concentration information re-

garding nonconcentrated producers does not contribute very much to the

understanding of the concentration effects. This is particularly true

when the buyers are relatively concentrated because concentration effects

are present despite the competitive structure of the suppliers. It

follows that the concentration of the buyers is more relevant in the

case where market power is a buyer-side problem. In many agricultural

industries market power is a buyer-side problem as commodity processing

industries have tended towards fewer firms and greater levels of con-

centration. However, the buyer side concentration data that are avail-

able are output measures of concentration rather than an input measure










which is needed. For this analysis it will be assumed that the output

measure is a proxy for buyer concentration in the buyer's industry. The

concentration ratio that will be used in the analysis is the largest

concentration ratio of the industries which make active hedging use of

a particular futures market. This ratio is a proxy for buyer or seller

concentration whichever is largest for a given market.

Earlier it was mentioned that concentration data are published once

every five years. Hence, there are missing concentration values during

the years 1961 through 1978 which is the sample period for the analysis.

For estimation purposes these missing values will be replaced by actual

concentration numbers. The problem that arises is the choice of numbers

one could use to replace the missing values. One possibility is to

assume that concentration makes a discrete jump every five years as the

intervening years show concentration levels that equal the most recently

published statistic. A preferred alternative is to assume that con-

centration levels adjust during the intervening years. A procedure

which permits adjustment is to linearly interpolate the data between ob-

served data points. This procedure is conducted as follows:

(4.4) INC = (COt + C0t+5)/5

(4.5) COt+i = COt + INC i = 1, 2, 3, 4


where INC = the annual interpolated adjustment in the level of

concentration,

COt = concentration ratio observed in period t,

COt+5 = concentration ratio observed in period t+5,

COt+i = calculated concentration ratios for years t+i.










If an end point is not available (i.e., COt or COt+5) then a linear

extrapolation is made. Say that COt+5 is not available because con-

centration ratios were last published in COt. In this case the ad-

justment is calculated as follows:

(4.6) INCE = (COt-5 + COt)/5

where INCE = the annual extrapolated adjustment in the level of

concentration for the years following t.

Like the case of market power, a similar empirical problem is faced

with the firm numbers variable. The number of firms that could use the

futures market as a hedging device is an unknown quantity. Typically,

there are a large number of producers of commodities that are traded on

futures markets. Many of these producers cannot use the futures market

because of size limitations and/or the market does not provide a

good hedge. Consequently, the number of firms that could use the market

among agricultural commodity producers is not known in general. The

same problem arises for agricultural processing industries. Although

statistics on firm numbers are readily available at the processor level,

the exact number of firms that could use the futures market is not known.

A survey of the commodity handlers, including producers, is needed to

derive a reasonable estimate of the number of firms using (or potentially

using) the futures market for hedging purposes. Leuthold [1975] has

conducted a regional survey which investigated the actual and potential

use of the live cattle futures market. However, this appears to be the

only empirical study found in the literature that concerns itself with

the measurement of potential firm use.

It was observed that firm numbers affect the transactions cost of

exchange, whether it be forward or spot. In addition the transactions










cost is some function of the number of buyers and the number of sellers.

An increase in the number of buyers increases the number of possible

exchange arrangements. Likewise an increase in the number of sellers

increases the number of exchange arrangements. If there are m sellers

and n buyers then the number of possible exchange arrangements is m x n.

The effect of an change of m and n on m x n is n and m, respectively.

An incremental change of the smaller of m or n has the largest effect

on the total number of possible exchange arrangements. In most agri-

cultural markets the number of sellers is very large relative to the

number of buyers. Consequently, an incremental change in the number of

buyers will have a profound effect on the transactions cost of forward

dealings. Thus, the information associated with the buyer side of these

markets should be "richer" with regard to the effect of firm numbers.

In the discussion of concentration the point was raised that there

are many industries using a given futures market. Usually, there are

dominant hedging users of futures markets. Feedlot operators, for

instance, are without question the dominant hedgers on the long side of

the feeder cattle market. A change in the number of feedlot operators

will affect the cost of forward dealings which will have a consequent

affect on hedging activities. The proxy for the firm number variable

that will be used in this analysis will be the number of firms in the

industry that is the dominant long hedger in a given market. It is

assumed that the proportion of these firms that could use the futures

market is fixed. Hence, changes in firm numbers bring about a pro-

portional change in the potential users. In addition, the implicit

assumption is made that the firm numbers of the dominant buying industry

are proportional to all buyer firms. Thus changes in the number of










firms in the dominant industry reflect a proportional change in all

buyer firms of all buying industries in a given commodity market. If

there are fewer sellers than buyers then the relevant seller side in-

formation will be used. Like concentration, firm numbers are observed

every five years for most of the commodities in the sample. The out-

lined interpolation and extropolation procedures used for concentration

will also be implemented to replace missing values.


Price and Basis Risk

A common empirical measure of price risk is a measure of price vari-

ability. In this study a measure of price variability will also be

utilized as a proxy for price risk. The appropriateness in using a price

variability measure as a proxy is uncertain since price risk is not

rigorously defined. Nonetheless the procedure adopted is within the

bounds of the current literature. The only problem associated with

price variability is choosing the appropriate measurement for this

analysis. A measure of price variability that will be used in this

analysis is the coefficient of variation. The virtue with this measure-

ment is that it is independent of the commodity units because price

variability is expressed as a percent of the mean price. Using average

monthly prices observed in a given year, the mean and the variance are

calculated for each commodity. It is assumed that hedgers and specu-

lators are responsive to intraseasonal risk. For instance, stockholders

are not so much concerned about next years prices as they are about

prices in the next couple of months. Feedlot operators generally pro-

vide the feeding service within a year's time which necessitates con-

cern regarding intraseaonsal prices. Speculators rarely hold a futures

contract for more than a year suggesting their attention to short run










price risk. As such, it is felt that average monthly prices used to

calculate an annual measure of the coefficient of variation is appropri-

ate.

In the theory chapter the argument that price risk encourages hedging

and speculative use of the futures market was put forth. Although this

is the accepted view it may not hold over all ranges of price variabil-

ity. For instance, in a market which is characterized by low price

variability the benefits of a futures market may be very small. In this

case hedgers may be willing to assume price risks, thus avoiding the

transactions cost of trading futures. Also, it may take a certain risk

level before the flexibility of liquid markets becomes advantageous

relative to other forward exchange mechanisms. Speculators, too, may

avoid markets with low price variability because the expectation of

windfall gain may not exceed the cost of transaction. Hence, low levels

of price variability may yield negative benefits to hedging and specula-

tive participation.

Telser and Higinbotham [1977] point out that price variability can

also increase exchange costs. This happens because exchange members who

trade for their clients are liable in case of default. Since the risk

of loss resulting from default is more likely to be greater in more

price volatile markets, the cost of operating an exchange is higher in

risky markets. This suggests that market activity should be inversely

related to price variability as the cost of promoting the activity is

an increasing function of price variability. In markets characterized

by fairly stable prices this is not a great problem. However, as the

degree of price volatility increases the risk of loss from default be-

comes more apparent. Thus, price variability has two effects on futures










market activity. First, it increases hedging and speculation activity

because it provides a liquid means of dealing with price uncertainty.

Second, it has the effect of increasing the cost of exchange operation.

Since it was suggested that at low levels of price variability the

benefits of trader use may be nominal, the total effect of price

variability on trading activity may be negative because of the cost

effect. In other words increases in price volatility at these levels

may increase the marginal cost of conducting trading on organized ex-

changes because of the increase in default risk. At higher levels it

is expected that the effect of price variability on marginal benefits

is heightened offsetting the cost effect. This leads to a positive

response of market activity to increase in price variability. It follows

that high levels of price variability will have a stronger impact on the

benefit side. With regard to the model the implied relationship between

market activity (e.g., hedging and speculation) and price variability

is nonlinear. Specifically, it is hypothesized that this relationship

is quadratic showing an inverse relationship at low levels of price

variability and a positive level at higher levels (Figure 4.1). It

could be argued that market activity should vanish in the absence of

price uncertainty which implies that the adjustment function shown below

emanates from the origin. However, the sample that will be used does

not include such markets indicating that the quadratic adjustment

hypothesis is appropriate for the data.

Basis variability would be a logical proxy to use for the conceptual

variable, basis risk. There is a problem with the empirical use of

basis variability, however. As an illustrative device consider the cash

and futures activities of a stockholder. Assuming all costs are zero the



















H


















0

Coefficient of variation (CV)

Figure 4.1--The effect of price variability on market
activity










stockholder's expected returnson the stocks purchased in period t

are:

(4.7) E(R) = ([ft E(Ft+I)] K + [E(pt+l) t]) X

where R = return,

f = futures price,

p = cash price,

X = stocks purchased,

K = proportion of stocks hedged.

In this example the stockholder acquires stocks in period t and sells

an appropriate number of futures contracts. The stockholder plans to

sell the stocks during period t+l. The variability of returns is given

by

(4.8) V(R)= (o2 + K22 2Kp a C0)X2

where V(R) = return variance,
2
2p = cash price variance,
2
o2 = futures price variance,

Ppf = cash-futures price correlation coefficient.

If it is assumed that p = af then 4.8 simplifies to the following:

(4.9) V(R) = X ap((l + K) 2Kppf)

In this case the individual can control risk exposure through control of

size (i.e., the choice of X) and the extent of the hedge (i.e., K). Now

consider the variability of the basis:

(4.10) V(b) = 2 + a2 2pf paf

where V(b) = basis variability.

If k=l then 4.8 is proportional to basis variability by a factor of X2










If kl/, then the relationship between V(R) and V(b) is more obscure

[Ward and Schimkat, 1979]. Consequently basis variability may not

reflect the risks of individual decisions to hedge because the X2 and

K are individually determined, whereas 4.10 is market determined.

An alternative to using basis variability as a proxy for basis risk

is the correlation coefficient between the cash and futures prices.

From 4.10 it is apparent that ppf is inversely related to the variance.

As such one would expect that a high correlation coefficient enhances

hedging activities because basis risk is reduced. The correlation co-

efficient is calculated from average monthly cash prices and the mid-

month closing futures price of the nearby futures contract. A corre-

lation coefficient is calculated for each year in the sample for each

of the included markets in the investigation. Note that V(b) is not

related to the parameter K whereas the V(R) is dependent on K.


Maturation

In chapter 3 it was argued that the evolving futures market ex-

periences a growth period in the early stages of development as hedgers

and speculators become more knowledgeable with regard to market use.

An obvious proxy variable for maturation is the age of a market. In-

formation regarding market age is readily available. Market age ranges

from 9 to over 120 in 1978 for the markets included in the sample. Five

markets were developed during the sample period which suggests the

effects of maturation are present in the data set. The effect of age

on hedging should be nonlinear given the maturation argument. A plaus-

ible relationship between hedging and age is characterized by the logis-

tic functional form (Figure 4.2). The effect of age is great when the

market is young but the effect approaches zero in an older more mature








































Age (AG)


Figure 4.2--The effect of age on hedging










market. The same adjustment process between age and speculation is

expected. However, Working [1962] has suggested that young markets

may require excess speculation to attract necessary hedging. This

effect, if it exists, would cloud the hypothesized relationship.


Perishability

The degree of commodity perishability was argued to be influential

on net benefits through its impact on hedging, speculation and volume.

Unfortunately, there are no precise empirical measures of perishability.

The commodities included in the sample vary with regard to perishability

ranging from very storable to semi-perishable in the case of fresh eggs.

A simple means of handling this problem is to classify the commodities

as storable or nonstorable. This classification can easily be adapted

to an econometric framework with the use of a dummy variable. A simple

classification, like the one proposed, avoids the arbitrary process of

defining a more refined classification scheme which would include inter-

mediate degrees of perishability. In addition, it is believed that the

simple scheme, which clearly distinguishes the storables from the non-

storables, is "rich" enough to show the effects of perishability. The

commodities included in the sample and their corresponding perishability

classification are listed in Table 4.1.


Government Effects

Several of the commodities included in the sample are included in

government price support programs. Whenever the commodity price drops

to the support level the government intervenes to forestall further price

declines. It was argued that as the commodity price nears the support

level short hedging activities are diminished. An empirical measure that










Table 4.1--Classification of commodities by degree of perishability

Storable Nonstorable

Wheat Live Hogs
Corn Live Cattle
Oats Feeder Cattle
Soybean Fresh Eggs
Soybean Oil
Soybean Meal
Potatoes
Frozen Pork Bellies
FCOJ
Cotton




will capture this effect is defined as follows:

(4.11) LN = (Loan Rate)/(Commodity Price)

This ratio ranges between 0 and 1. As the ratio approaches one the effect

of the price support program on hedging activities becomes more acute

since the cash price is near the support level implying minimal risk of

further price decline. Loan rates are subject to change from year to

year. Consequently, annual observations are made on LN for each commodity

market. The commodity price used is the mean price calculated from 12

monthly observations during the year in which the respective loan rate

is observed. The effect of LN on hedging is depicted in the figure be-

low (Figure 4.3). At low levels LN has no effect on hedging, but as LN

approaches one, hedging activity is severely affected.


Opportunity Cost and Intermarket Spreading

In the theory chapter it was argued that holding futures contracts

was one of many possible investment opportunities facing speculators.

Consequently, speculative involvement in the futures market will be