Implications of option markets

Material Information

Implications of option markets theory and evidence
O'Brien, Thomas J. ( Thomas Johnson ), 1947- ( Dissertant )
Place of Publication:
Gainesville, Fla.
University of Florida
Publication Date:
Copyright Date:
Physical Description:
vi, 154 leaves : ill. ; 28 cm.


Subjects / Keywords:
Call options ( jstor )
Financial portfolios ( jstor )
Financial securities ( jstor )
Investors ( jstor )
Options contracts ( jstor )
Options trading ( jstor )
Standard deviation ( jstor )
Statistical discrepancies ( jstor )
Stock prices ( jstor )
Systematic risks ( jstor )
Capital market -- Mathematical models ( lcsh )
Dissertations, Academic -- Finance, Insurance, and Real Estate -- UF
Finance, Insurance, and Real Estate thesis Ph. D
Option (Contract) -- Mathematical models ( lcsh )
bibliography ( marcgt )
non-fiction ( marcgt )


Many new markets for -options have opened in recent times. Financial research has been interested in the experiences of option investors, and much empirical work has been done in this regard. However, in previous studies the issue of why options exist at all is rarely addressed. The exploration of the role of options in important theoretical models of capital market equilibrium leads to the following conclusion concerning the effects of opening new option markets: If margin ceilings impose effective borrowing constraints on any investors, then the expansion of the option market will cause the equilibrium intercept of the risk-return relation to shift downward. This theory is tested in this study by an examination of a time series of returns of option-stock hedges that theoretically have zero systematic risk. The hedge returns, which empirically did not appear to evidence significant systematic risk, did behave in a manner consistent with the theory of the risk-return intercept change. However, the change in the hedge returns was not statistically significant.
Thesis (Ph. D.)--University of Florida, 1980.
Includes bibliographic references (leaves 148-153).
General Note:
General Note:
Statement of Responsibility:
by Thomas J. O'Brien.

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University of Florida
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Copyright [name of dissertation author]. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Resource Identifier:
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AAL5573 ( NOTIS )


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I would like to thank the following people for their

patience and technical assistance: Drs. Robert C. Radcliffe

(chairman), Richard H. Pettway, and W. Andrew McCollough

of the Finance Department; H. Russell Fogler of the

Management Department; and James T. McClave of the

Statistics Department.





I INTRODUCTION . . . . . . . .
Futures and Options . . . . . .
Use of Options in the U.S. . . . . .
Evolution Prior to 1973 . . . . .
The Post 1973 Option Market . . . .
The Moratorium and After. . . . .
Reasons for Listed Stock Option Usage .
Evidence of Reasons for Recent
Option Trading . . . . . .
Overview of the Study. . . . . . .
Notes . . . . . . . . .

Equilibrium with One Option and No
Riskless Asset . . . . . .
Equilibrium with One Option and Unrestricted
Trading in the Riskless Security. . .
Equilibrium with One Option and a Margin
Constraint on Riskless Borrowing. . .
Notes . . . . . . . . .

Introduction . . . . . .
Klemkosky and Maness . . . .
Hayes and Tennenbaum . . . .
Reilly and Naidu . . . . .
Trennepohl and Dukes . . . .
Implications . . . . . .
Notes . . . . . . .

Excess Returns . . . . . . .
Zero Systematic Risk Returns . . .
Neutral Spread Returns. . . . .
Previous Empirical Results of Neutral
Spreads . . . . . . .
Neutral Hedge Returns . . . .














Previous Empirical Results of Neutral
Hedges . . . . . . .
Direction of the Methodology. . .
Two Further Considerations. . . .
Selection of Time Span, Holding Period,
and Data . . . . . . .
Time Span: November 30, 1973--August
29, 1975 . . . . . .
Holding Period Assumption: Monthly
Observations . . . . .
Description of the Data . . . .
Preliminary Procedure . . . .
Neutral Hedge Returns . . . .
Naive Diversification . . . .
The Hypothesis Test. . . . . .
Preliminary Test Procedure. . . .
Two Additional Procedures . . .
Notes . . . . . . . .

Introduction and Summary of Results. . .
Naive Diversification . . . . .
Systematic Risk in Hedges?. . . . .
The Minimum Variance Zero Beta Portfolio?
Time Series Hypothesis Tests . . . .
First Version: Tests Without the Index
Second Version: Tests with Index . .
Notes . . . . . . . . .

A Test of the CAPM . . . . . . .
Systematic Risk of Neutral Hedges. . . .
Notes . . . . . . . . .






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S 121






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



Thomas J. O'Brien

December, 1980

Chairman: Robert C. Radcliffe
Major Department: Finance, Insurance, and Real Estate

Many new markets for options have opened in recent

times. Financial research has been interested in the

experiences of option investors, and much empirical work

has been done in this regard. However, in previous studies

the issue of why options exist at all is rarely addressed.

The exploration of the role of options in important

theoretical models of capital market equilibrium leads to

the following conclusion concerning the effects of opening

new option markets: If margin ceilings impose effective

borrowing constraints on any investors, then the expansion

of the option market will cause the equilibrium intercept

of the risk-return relation to shift downward. This theory

is tested in this study by an examination of a time series

of returns of option-stock hedges that theoretically have

zero systematic risk. The hedge returns, which empirically

did not appear to evidence significant systematic risk,

did behave in a manner consistent with the theory of the

risk-return intercept change. However, the change in the

hedge returns was not statistically significant.


The expansion of new option trading since April, 1973,

has been rapid and still continues. The purpose of this

study is to examine a potential effect that the expansion

may have had on the capital markets. The hypothesis is

that new option trading causes a change in the intercept

location of the theorized risk-return relation. The

theoretical framework used for the development of the

hypothesis is the mean-variance capital asset pricing model


The bases for the theory that options cause a change

in the intercept of the risk-return relation are two alleged

circumstances (to be analyzed later): (a) options may be

employed by some traders to obtain leverage amounts that

are not possible through normal margin channels; and (b)

in a single-period, equilibrium-theoretic model where mar-

gin constraints are effective, the introduction of new

options may cause a shift in the intercept of the risk-return

relation. The first of these observations will be examined

in this chapter; the second observation is considered in

Chapter II. Chapter III is a discussion of other empirical

work that is potentially related to this study. In Chapter

IV a methodology for empirically testing the hypothesis is

described in detail. The results of our own tests are

presented in Chapter V. Chapter VI concludes the study by

reviewing the major results and suggesting various implica-

tions and limitations. First, the nature of options is


Futures and Options

In a treatise by Bachelier (1900), translated from the

French by Boness and included in Cootner's (1964) book,

options are described as being futures contracts but with

a limited liability feature for one of the contract parties.

Trading in a futures contract is similar to spot trading

in the commodity, except that in a futures contract the

exchange of money for the commodity is made at a specified

later date. The price at which the exchange is to be made

is agreed upon when the contract originates, however, and

not at the subsequent time of exchange. An option is

similar to a futures contract except that one of the ex-

change parties has the option to cancel the future trade,

and payment for this privilege is made at the time the con-

tract originates.

In all futures contracts there is a seller, who con-

tracts to deliver the commodity for money, and a buyer,

who contracts to delivery money for the commodity. If the

buyer has an option to cancel the trade, then it is said

that the buyer owns a call option. In this case the seller

of the commodity is the writer of the call option. If the

commodity seller has the option to cancel, then it is said

that the commodity seller owns a put option. In this case

the commodity buyer is the writer of the put.

The above description of options applies to "European"

options. Alternatively a call or put option may be an

"American" option. An American option is the same as a

European option except in an American option the option

owner may call for the exchange (still at the agreed price)

anytime prior to the original settlement date. Since the

settlement date is the final time at which an option owner

may announce his option, this date is termed the expiration

date of the option.

It is from these characteristics that the following

definitions of call and put options have evolved: A call

option is a contract giving its owner the right to buy a

commodity at a specified price, at or before a specified

time (depending on whether it is European or American);

a put option is a contract giving its owner the right to

sell a commodity at a specified price, at or before a

specified time. In either case the right of contract is

granted by the writer of the option. If an option contract

is held beyond the expiration time, it becomes worthless.

In the case of either calls or puts, the option owner

must pay to the writer an amount for the limited liability

privilege; and in the case of an American option, the

option owner must sometimes pay to the writer an additional

amount for the right to activate an early exchange. Merton

(1973b) has argued that in an ideal market, this additional

payment for the early exercise privilege should be zero, if

either (a) the underlying commodity pays no dividends prior

to expiration time, or (b) the futures (settlement) price is

automatically adjusted for any dividend payments. In the

U.S. market for exchange listed common stock options there

is no dividend protection, and since many underlying stocks

do pay dividends, the early exercise privilege theoretically

has some value to the owners of some options traded on the

U.S. exchanges.2

In the case of futures contracts without options the

only thing to be negotiated is the future exchange price;

for options there are two amounts to be negotiated simul-

taneously: (a) the future exchange price, and (b) the amount

that the holder of the option will give to the writer for

the limited liability and early exercise features. In the

present U.S. exchange listed option market, the future ex-

change price for stock in dollars is contract-standardized.

What is negotiated is an amount called the premium. Thus,

the premium is consideration for three amounts: (a) the

price of the limited liability feature; (b) the price of

the early exchange feature; and (c) the difference between

what would have been the negotiated futures settlement price

and the contract-standardized settlement price.3


Use of Options in the U.S.

Evolution Prior to 1973

Although put and call options originated outside of

the U.S., the use of put and call options in the U.S. was
begun by financier Russell Sage in 1869. Sage and others

were in business to lend money to brokers, who would use

the borrowed funds (termed margin credit) to purchase

stocks. Experience showed that the brokers could not always

repay the loans when due, but it was impossible for the

lenders to charge enough interest to compensate for this

risk because of usury laws. Consequently, Sage devised a

way to employ call and put options to conduct his business,

and other lenders soon adopted Sage's method. Instead of

extending margin credit to a broker, the lender would write

a call option, and the broker would become the call option

owner. As part of this arrangement the broker would write

a put option back to the lender. Thus if the stock declined

in price, the lender could exercise his put option and sell

the stock to the broker at the put contract price. If the

broker could not pay, then this was the lender's loss. But

the lenders could charge what they wanted for the call

options, and presumably they charged enough to compensate

for the possibility of nonrepayment.5

The put and call business operated along these lines

until Sage died in 1906. Sage met all of his contractual

obligations, but his successors would, from time to time,

dishonor the contracts if the market went against them as

sellers. These reneges caused great resentment among

option traders, and the business was plagued by lawsuits

until the Securities and Exchange Act of 1933-1934. At

that time various dealers in puts and calls formed' the

Put and Call Brokers and Dealers Association, Inc. This

association brought two vital attributes to the business:

(1) uniform contracts and (2) endorsement by New York

Stock Exchange members.

The new market in options provided by the Puts and

Calls Brokers and Dealers Association extended option

trading to individuals as well as brokers. Individual

investors were permitted to own or write puts and calls

separately. However, the market involved the direct

matching of owners and writers. The large transaction

costs of the matching procedure for the separated contracts

provided the impetus for the first option exchange in 1973.

The Post 1973 Option Market

The first U.S. option exchange created for the purpose

of facilitating option trading was the Chicago Board Options

Exchange (CBOE). Other option exchanges subsequently began

operations following the unexpectedly huge success of the

CBOE. The activities of all option exchanges are presently

coordinated by the Options Clearing Corporation, which also

serves as guarantor to individuals on both writing and

owning sides of option contracts.

Exchange listed options are now standardized in the

following respects. Exchange listed option contracts are

for 100 shares. Premiums are specified in dollars per

option on one share, so that a contract quote of $4 would

mean that the buyer would pay a premium of $400 for the

standard contract on 100 shares. The standardized option

contracts which are traded on the organized exchanges are

quoted by the month of expiration and by the price at which

the future stock transaction will take place--the exercise

price. At present, these contracts expire at 11:50 p.m.

Eastern time on the Saturday following the third Friday of

the expiration month. The cut-off-time for individual in-

vestors to instruct brokers concerning exercise is 5:30 p.m.

Eastern time on the business day immediately preceding the

expiration time.

Example: An American Telephone and Telegraph Company

(AT&T) January 50 that is traded on the

Chicago Board Options Exchange (CBOE) is

an American call option contract to buy 100

shares of AT&T corporate common stock. The

price at which the 100 shares of stock may

be bought is $50 per share, and the stock

may be bought any time before the January

expiration time. If the price of a share

of AT&T exceeds $50 just prior to the

cut-off-time, then it would be profitable

for the call owner to exercise the option

and resell the shares, assuming no trans-

action costs. Net trading profit depends

upon the original cost of contract (the


If the price of a share of AT&T is

below $50 per share near the cut-off-time,

then it would make no sense to exercise the

option and pay $50 per share, when the same

share could be purchased for a lower price

without an option. The trader in this case

should let the contract expire worthless.

The net loss will be the original purchase

price, or premium.

An AT&T January 50 P is a put contract

with the same specification, except a put

contract is a contract to sell 100 shares.

With puts, the trader should exercise if

the stock price, at cut-off-time, is below

the exercise price. The trader will make a

net profit, if the stock price is below the

exercise price by more than the original

premium paid. If the stock price is above

the exercise price at cut-off-time, the

trader should discard the worthless put.

End of Example.

The option market is organized in such a way that

individual investors can take profits without exercising

their options at the expiration time; the option owner, at

the expiration time, simply sells the contract back to a

writer for exactly the difference between the stock price

and the exercise price. This difference is the owner's

gross profit or loss. Whatever is the owner's gross profit

is the writer's loss, and vice versa.

Because of the efficiency of the options exchange there

is usually good liquidity in the sense that an option con-

tract, once originated, can easily be resold to another

trader anytime prior to the expiration time. The considera-

tion in any purchase-sale of an option contract, the premium,

is negotiated virtually continuously according to supply and

demand. Thus option traders can incur profits and losses by

buying and selling option contracts over any time intervals,

without ever seriously considering taking delivery of the


The prices of the last contracts traded of each option

type on each stock are reported daily in financial and

metropolitan newspapers. Presently, transactions costs are

not included in the newspaper trade quotes. Transaction

costs vary with the size of the transaction in both dollar

and volume terms. Brokerage commission schedules are com-

petitive and will vary from broker to broker. Transaction

costs are significant to nonexchange members who want to

trade a few contracts; however, transaction costs are less

significant to exchange members. The consequences of

present tax structures for studies of option trading are

complex to analyze, because there are different tax rates

for different investors. More detail on the arrangements

for common stock option trading may be obtained by consulting

Gastineau (1975), Golden (1975), the Chicago Board Options

Exchange Prospectus (1973), and the Options Clearing Cor-

poration Prospectus (various dates).

The Moratorium and After

Trading volume in contracts of each underlying stock

and the number of underlying stocks had expanded rapidly

and steadily until July 1977, when the Securities and Ex-

change Commission (SEC) halted the further expansion of

options trading on additional underlying securities. At

the time of the moratorium, call option markets on common

shares of 235 different companies were open, and put option

markets on 25 different companies were open. Volume of

underlying shares represented by option trading continued

to increase after the moratorium on expansion was imposed

by the SEC.

The purpose of the moratorium was to give the SEC a

chance to examine the effects of option trading and to

review the trading and self-regulating practices on the

exchanges. The result of the SEC study was The Report of

the Special Study of the Options Markets to the Securities

and Exchange Commission (hereafter called the SEC Study),

which was dated December 22, 1978.

In general the SEC Study (p. v) found that "options

can provide useful alternative investment strategies to

those who understand the complexities and risks of options

trading. But, since regulatory inadequacies in the options

markets have been found, the Options Study is making

specific recommendations needed to improve the regulatory

framework within which listed options trading occurs and to

increase the protection of public customers."

In 1980 the moratorium was lifted, and in May of 1980

the list of stocks underlying listed put options expanded

from the original 25 to 105. In July, 1980, the list of

new stocks underlying call and put options also began to

expand again.

Exchange listed options on physical commodities,

foreign currencies, or other securities (such as treasury

bills, bonds, mutual funds, and futures contracts) are not

presently traded in the U.S. However, options on physical

commodities are traded in other countries, including gold

futures options on the Winnepeg, Canada Exchange.

Reasons for Listed Stock Option Usage

Various reasons have been offered about why investors

and speculators trade options. Uses of options are ex-

plained in passages and chapters of various investments

textbooks Cespecially in the more descriptive textbooks)

and are more thoroughly described in some of the less
technical books about options. Some typical rationales

for the use of options by individual (non-broker)

investors are developed below. The interested reader is

advised to consult the sources in note 7 for more details.

Leverage. The use of borrowed funds to purchase in-

vestments is referred to as financial leverage, or more

simply leverage. The use of puts and calls by brokers as

an alternative to margin arrangements has already been


In modern markets margin credit may also be employed

by individuals, who borrow the funds from brokers or banks.

For individuals, the maximum amount of credit that can be

employed to purchase stocks is set by Regulations T and U

of the Federal Reserve. CCurrently the "margin ceiling"

is 50%, meaning no more than 50% of a stock's purchase

price may be paid with borrowed funds. This ceiling has

ranged from a low of 40% to a high of 100% in the last 40

years.) Investors who employ option contracts for leverage

instead of margin credit have three distinct advantages:

(1) Since an option contract premium will often be between

1% and 50% of the cost of the stock shares, option contracts

represent much higher degrees of leverage than are available

through the use of margin. (2) Option contracts offer a

limited liability feature which prevents loss of more than

the original premium paid. Such limited liability is not

available in margin trading. The limited liability feature

of knowing one's maximum loss in advance may be an induce-

ment to individuals to employ the very high leverage. (3)

Standard brokerage policy requires a minimum equity of

$2,000 for margin trading in common stock. Thus option

trading may bring into the market some new investors, who

individually may have relatively small amounts of capital.

Hedging. If a stockholder becomes uncertain about the

future price volatility of a stock, or if the stockholder

anticipates a price decline, writing (selling) call options

would give the investor a position that is hedged against

unfavorable changes in those variables. The investor can

use his stock as collateral, thus temporarily changing the

strategy of his position, without incurring the transaction

costs of selling and buying stocks and without foregoing


Income. Although it is theoretically possible for

investors to sell shares for current income purposes,

this practice is not normal. The costs of transactions

and the possibility of deferring taxes work to discourage

share turnover. One way for stock owners to receive cur-

rent income is to write call options utilizing stock shares

as collateral. This technique is also useful for managers

of large institutional portfolios, for whom the sale of

shares is difficult without creating a significant stock

price decline, due to the sheer size of the average trans-


According to Paul Sarnoff C1968), a former options

broker for many years, the scenarios above describe the

primary nonbroker uses for options. Thus, in general,

option buyers are thought of as speculators who employ

options because there is more leverage than otherwise avail-

able in the stock market (with limited liability), and who

are tolerant of risk and/or possess special information.

Option writers are primarily portfolio managers who desire

protection against large price drops, especially during

times of income need. Sarnoff's beliefs about why options

are utilized correspond to the evidence reported next.

Evidence of Reasons for Recent Option Trading

The SEC Study identified three categories of partici-

pants in the options markets: (1) public nonprofessionals,

(2) professional money managers, and (3) professional traders

and arbitrageurs. The SEC Study also identified the basic

purposes served by the various common types of options

transactions. The basic purposes are: (a) to obtain

leverage, (b) to hedge positions in the underlying security,

(c) to increase current income from securities holdings,

(d) to arbitrage for profit, (e) to speculate or trade on

perceived over-and-undervalued situations, and Cf) to

facilitate the provision of brokerage and market-making

services in the underlying stocks.

The SEC Study (pp. 106-107) describes the varying

perspectives of investors as they approach the market:

Traders, for example, attempt to capitalize
on undervalued and overvalued situations by using
complex mathematical models and computer techniques
to detect and arbitrage against perceived illogi-
cal divergences in prices. Studies of option price
patterns, however, indicate that while price
divergences do occur which may provide profitable
trading opportunities for professionals the
divergences generally are too small for trading

opportunities by members of the public because of
transaction costs. Other, generally sophisticated,
investors perceive an opportunity to adjust the
risk-reward mix of their portfolio of assets in a
more precise manner because of the additional com-
binations of risk and potential return opened up
to them by the availability of exchange traded
S Risk management and risk adjusted performance
have become basic criteria upon which professional
managerial ability is evaluated. Most individual
investors in options, however, are probably using
option purchases and sales as a substitute for
stock purchases and sales. Dealing in options
enables them to take short-term positions in the
stock, or shift out of the stock in the short-term
with lower transactions costs; and, for buyers,
it offers greater leverage than would be the case
if they were trading directly in the underlying
securities. (SEC Study, p. 107)

A survey released in 1976 and conducted by Louis Harris

Associates (1976) for the American Stock Exchange listed 10

strategies that appear to be most commonly employed by in-

vestors. The 10 strategies listed by Harris are shown be-



1. Buying options in combination with stock ownership.

2. Buying options in combination with fixed-income

3. "Pure" buying of options without underlying stock
or fixed-income securities.

Mixed Strategies

4. Buying options against a short position in under-
lying stock.

5. Buying options as a hedge against a short position
in securities related to the underlying security.

6. Selling options hedged against other related

7. Spreading options by buying and selling different
options in the same underlying securities.


8. Selling fully covered options.

9. Selling partially covered options.

10. Selling completely uncovered options.

The Harris survey found that among individual investors,

the largest percentage (58%) employed the pure buying of

options strategy (#3 above). Of the persons investing a

total of $2,500 or less, 49% employed the pure option buying

strategy. In contrast to individual investors, 79% of the

institutional investors surveyed concentrated their activities

in fully covered option writing strategies. Another survey,

by Robbins, Stobaugh, Sterling, and Howe (1979), sponsored

by the CBOE, also found that the two strategies followed

most frequently by investors were the simple buying and

covered writing of option contract strategies. The SEC

Study CP. 116) pointed out that: "Neither survey included

interviews with broker-dealers, a professional, but extremely

important group, using options in their activities. Block-

positioning firms, marketmakers and other broker-dealers

make extensive use of options in providing dealer services

to the public market."

From the foregoing discussion it appears reasonable

to hypothesize that Ca) margin constraints are effective

in the U.S., and (b) one rationale for option owning is

that options offer a viable alternative to margin trading

as a means for more investors to obtain more leverage.

Overview of the Study

In the next chapter the effects of introducing options

into a theoretical model of risk and return are analyzed.

One assumption of the model is an effective margin con-

straint. It is found in the model that as more options

are introduced, the equilibrium risk-return relation is

altered; specifically, the expected return on securities

with zero systematic risk will decline toward the riskless

rate. This result is not surprising in light of the results

of Sharpe (1964), Lintner (1965), Black (1972), Fama (1976),

and Vasicek (1971), who originated and contributed heavily

to the construction of the theoretical model. Further de-

tails are deferred to Chapter II.

In Chapter IV the methodology is developed to test the

hypothetical effect of options on the risk-return relation.

The reader may be interested in a brief overview of the

procedure to be employed:

First are identified securities which are logical

candidates for a zero systematic risk portfolio. The rate

of return on a zero systematic risk securities portfolio

will be referred to as ECr ). Estimates of that expected

rate are denoted .

The time span of a time series of ra values covers an

extended period during which no new options began trading

on exchanges (Period 1) and another extended period during

which an ample number of new options began trading (Period 2).

During Period 1 the time series of observations for r

may be considered as estimates of an equilibrium rate,

assuming there are no changes in market equilibrium condi-

tions. During Period 2 the time series observations of r

must be viewed, in light of the hypothesis, as including

two portions: (1) the new lower equilibrium rate of return

for zero systematic risk portfolios, and (2) the return

associated with the transition from the old equilibrium

state to the new one, following the theorized effect of the

new options.

The second portion may be significantly higher than

either of the equilibrium rates as is evident from the

following example: Assume that prior to the introduction

of new option trading the equilibrium rate of return on

a zero systematic risk asset is .08. Now suppose the

introduction of new option trading causes the equilibrium

rate for the zero systematic risk asset to decline to .06.

In order for this to occur, the price of the zero systema-

tic risk portfolio must increase by 33-1/3%, say from 100

to 133-1/3. The observed transition rate of return would

show up in the Period 2 time series and be relatively high


This scenario establishes (qualitatively) what should

be expected from the empirical analysis if the following

assumptions are valid: (1) the theoretical framework

employed in Chapter II is valid; (2) other factors that

may affect E(r ) are properly accounted for; and (3) new

option trading has a significant enough impact to be ob-


In order to accomplish the purposes of the empirical

analysis, these general procedures are to be employed:

(1) The time series of observations of F are con-

verted into "excess return" form by subtracting corresponding

observations of rf. This adjustment represents a method of

accounting for exogeneous shifts in the location of the risk-

free rate, and simultaneously of the level of r Such

shifts could result from federal influence on interest rates,

for example. Whatever the exogeneous sources of disturbance,

it is the relative distance between r and rf values that is

being measured in this study, so the conversion to excess

returns is appropriate. This excess return variable is
referred to as r. Thus, abnormally high observations of
-e re
r are expected to be found in Period 2 relative to Period 1.

(2) In order to gauge the significance of the values
of rE in Period 2 relative to Period 1, the following

statistical procedure is employed. The observations of
re are used as dependent variable observations in a multiple

regression on two variables called I1 and 12. The first

variable, Ii, takes on the value 1 for all observation

periods in Period 1 and Period 2. The second variable, 12,

takes on the value 1 for all observation periods in Period 2,

but has the value 0 for all observation periods in Period 1.

The resultant regression coefficient for II, will be the
mean of the -e time series during Period The resultant
mean of the r. time series during Period 1. The resultant

regression coefficient of the variable 12 is the increase
in the mean of the r values from Period 1 to Period 2.

Thus, the t-statistic of the regression coefficient of I2

can be used to judge whether the values of the observations
of r are significantly higher in Period 2 than in Period 1.

(3) The possibility exists that equilibrium shifts

will occur as a result of factors other than new option

trading, such as changes in expected inflation or in multi-

period preferences. One indication of this possibility is

for the residuals in the regression to exhibit serial

correlation. If this correlation occurs, then the meaning-

fulness of the regression is in question. A well-known

statistical procedure (Cochrane-Orcutt) may be employed

to counteract serial correlation in the residuals and what-

ever effects this correlation may have on the t-statistic

of the coefficient of 12.

The next chapter presents the theoretical basis for

the hypothesis.


There is only one delivery date, which is decided upon
at the time the contract originates. The delivery date is
also called the settlement date.

Empirical examinations of option pricing models in-
dicate that models which adjust for dividends are more valid
than option models with no dividend adjustment. See, for
example, Galai (1977) and Chiras (1977). Option pricing
models are discussed in Chapter IV.

For example, consider a call option, and let the prices
of the limited liability feature and the early exercise
feature be $.50 and $.25, respectively. Assume the contract
standardized settlement price is $50.00, but that two traders
would have preferred to negotiate a futures price of $52.00.
The buyer of the call option must pay $2.00 more for the
contract than he would have if the settlement price were
$52.00. Thus the call contract premium should be $2.00 +
.50 + .25 = 2.75 in this case.

This account of option trading in the U.S. prior to
1973 is paraphrased from Sarnoff (1968).

As long as risk conditions dictated that lenders re-
quired a rate of interest less than the usury law ceiling,
lenders could have used either the option method or the
direct lending method. In order to obtain higher "interest"
compensation if the option method was chosen, lenders would
build this compensation into a higher price of the call
option. For example, a call option would sell for more if
conditions dictated- 10% interest than if conditions dictated

See Phillips and Smith C1980) for a discussion of
transactions costs of options exchanges.

Examples of less technical investments textbooks which
discuss option strategies are Johnson (1978), Mendelson and
Robbins (1976), and Wright (1977). The more recent the text,
the more detail about options can usually be found. Recent
technical investments texts, like Sharpe (1978) and Francis
(1976), usually have a formal chapter about stock options.
Examples of nontechnical books about options include Clasing
(1975), Cloonan (1973), Dadekian (1968), Filer (1966), and
Sarnoff (1968). In addition, Malkiel and Quandt (1969) is
written for a wider audience than only options experts and
is detailed in its explanation of various option trading


An interesting example of the use of options by bro-
kers to arbitrage for profit and acquire capital appeared in
an article of the Wall Street Journal (August 7, 1980, p. 30).
Brokers went long 1000 calls and 1000 puts on the shares of
Tandy Corporation. Since the prices were $9 per call and $3
per put, the "spread" was $6. Brokers are required to outlay
only the amount of the spread for this position. The brokers
then sold 1000 shares of the stock short at $66 to create a
perfect hedge position that would return $6 per share, or
$600.00. The brokers could then use the short sale proceeds
to invest elsewhere. Since the short sale proceeds were
$6.6 million, brokers had $6.0 million in "free" capital,
upon which no interest had to be paid. In this case the
brokers were exploiting a riskless arbitrage situation.


This chapter introduces options into the analysis of a

single period capital market model. Equilibrium conditions

are derived in the usual fashion, except that investors are

permitted to hold options in their portfolios as well as

stocks. A primary distinction between stocks and options in

this context is that options are not issued by corporations,

whereas stocks are. The aggregate market value of all

option positions in the market is zero; for every investor

who owns an option, there is another investor who is short

an option. The aggregate market value of each stock in the

market is not constrained, but must be positive, of course,

to be realistic.

Three circumstances will be examined: (i) the case

where no riskless security exists; (ii) the case where

investors can go long or short the riskless security in any

amount; (iii) the case of margin restrictions on trading

the riskless security. The riskless security is defined to

be a security with a fixed and known-in-advance nominal

return. As with options, the aggregate holdings of the

riskless security are presumed to be zero. In other words,

the economy as a whole cannot have an excess of borrowing

over lending or an excess of lending over borrowing.

The models to be derived are single period equilibrium

models based upon the mean-variance criterion of Markowitz

(1959). In addition, it will be assumed that (a) all

investors have identical probability beliefs;1 (b) all

investors make their portfolio decisions at the same dis-

crete points in time; (c) the market is perfectly void of

indivisibilities, taxes, transactions costs, and monopoly

influence by any investor; and (d) no consumption price

inflation exists.2 Thus, this chapter applies an elementary

Sharpe-Lintner CAPM framework to a setting that includes


The analyses in the three sections of the chapter

correspond to three distinct circumstances mentioned earlier

in connection with the trading of the riskless security.

In all three sections only one option is assumed at first.

Generalization is subsequently made to a portfolio of

options with the following result: if the riskless security

is either unavailable or restricted (and if investors are

constrained by the latter circumstance), then the equilib-

rium expected rate of return on zero systematic risk secu-

rities declines as the number of options in the market


In the proofs to follow investors are assumed to employ

in their portfolios any of n risky assets (stocks), the

option3 and, when specified, a riskless security. The

random rates of return on the assets are denoted rl, r2, ..,

rn, while the rates of return on the option and the riskless

security are denoted by ro (a random variable) and rf, res-

pectively. Similarly, the proportionate investments by an

individual into each of the n risky assets are denoted by

X1, x2, .., Xn; xo and Xf denote the proportionate holdings
of the option and the riskless security, respectively. For

any investor the total of his portfolio proportions must be

equal to 1, i.e.,

xo + Xf + E xi = 1. (1)

By definition, an investor's portfolio expected rate of

return is given by:

E(r) = xiE(ri) + xoE(ro) + xfrf. (2)

An investor's portfolio variance is given by:

2 n n n
2 = xx2cov(ri + 2x x cov(ro,ri). (3)
i=lj=1 i=1

The above relationships will be employed in all three

of the sections to follow.

Equilibrium with One Option and No Riskless Security

In the first case to be considered, xf is constrained

to be zero, since trading in the riskless security is not

permitted. Under this circumstance, the only securities

available for trading are the n risky assets and the option.

Substituting xf = 0 into equation (1) and rearranging, we

find that:

xo = 1 xi, (4)

if there is no riskless security. Now equations (2) and (3)

can be applied to the no riskless security case by the sub-

stitution for x from equation (4) to get (a superscript k

has been added to denote the kth investor):

k n k n k
E = xiE(ri) + (1 Z xi)E(r0); (5)
i=l i=l


k 2 n n k k n k 2 2
(a ) = x x.jcov(r ,r) + (1 x.) a
1 i=lj= 1 i=l1

n k n k
+ 2(1 Z x.) Z x.cov(r ,r ), (6)
i=l I i=l1 o

where the numerical subscripts in the terms to the left of

the equal signs in equations (5) and (6) are references to

the section of the chapter.

Thus the investor's portfolio problem of minimizing

variance for each level of return can be solved by mini-

mizing the following Lagrange function constructed out of

equations (5) and (6):

k k 2 kk n n k
L = (or ) + X [E(r) x1E(ri) (1 Z xi)E(ro) (6a)
1 1 i=1 i i=1

where Xk is the Lagrange multiplier for investor k.4

Substitute equation (6) for the expression (a )2 in equa-
k r
tion (6a), differentiate L1 with respect to each of the n

portfolio weights of the n risky assets, and set the

derivatives equal to zero. The result is equation system

(7.1) through (7.n) below:

dLk nk
= 2[ E x.cov(r ,rj)
k 1=1 k
dx n
+ (1 E x

n k 2
(1 Z x.)o
i=l 1
k n k
i)cov(r ,r1) jcov(ro,rj)]
o 1 j=1

- k[E(rl) E(ro)] = 0;

dL n
1 = 21 E x.cov(r ,rj)
Sk j=1
dx2 n
+ (1 Z x

-(1 Z xk)o2
k n k
.)cov(r ,r2) x cov(r ,r.)]
1 j=l j o j

- [E(r2) E(ro)] = 0


dL1 n k n k 2
S 2[ Z x.cov(r,rj) (1 xi)o
Sj=li=l o
dx n k n k
n + (1 xi)cov(ro,rn) Z x cov(r,r)]
i=l j=1 j 0

[E ) E(r )] =
- X [E(r ) E(r)] = 0


0. (7.n)

It is from equations (7.1) through (7.n) that market

equilibrium conditions may be derived as follows:

It is useful to first expand equation (7.1) as (7.1a)


k k k
2[(x1cov(r,r) + x2cov(r,r) + ... + xnov(rr

k k k2 2n
-(1 xk x ... x
1 2 n o

- xcov(rr) x kcov(r,r2) ... xncov(r,r (

k k k
+ (1 I X - .. x )cov(ro,rl)]

k[E(rl) E(ro)] = 0-.

Assume there are a total of P investors. Let w be

the proportion of total market wealth represented by the kth

investor's wealth. Multiply equation (7.1a) by wk for each

of the investors to get:

2w [x1cov(rl,rl) + x2cov(rl,r2) + ... + xncov(r,r)

k k k k 2
-2w [1 x x2 - x ] (7.1b)

kk k k
-2w [x cov(ro,rl) + x cov(r ,r2) + ... + x cov(ro,rn)]

wk k k k wkk[
+ 2w [1 xl x2 ... ]cov(r r ro,rl) w E(r ) E(r0)]

= 0.

Next sum the P weighted first order conditions over all

investors in the market to get an aggregated version of the

first first order condition:

P k k P k k
2cov(rl,rl) Z w x + 2cov(rl,r2) w 2+ ...
k=l k=l
Pk k
+ 2cov(rlrn) w n
Pk P kk P P k k k 2
-2[ Z w w x- I wx ... X w x
k=l k=l k=l 2 k=l o
P k P k (7.1c)
-2[cov(r ,rl) Z wkx1 + cov(r ,r2) wx + .. 7.
k=l k=l
k k
+ cov(ro,rn) w x
o n k=l
2ck P k kk k P k k
+2cov(r rl)[ Z w Z w xI w x21 ... Z w Xn
k=l k=l k=l k=l
k k
-[E(rl) E(r )] w = 0.

By definition, wk= 1. Also, Z w xl is the propor-
k=l k=l
tionate weight of the first security in the market port-
k k
folio. Similarly, Z w x2 is the weight of the second
security in the market portfolio; and so on for all risky

assets. Let these weights be denoted xm, x2, ..., xm

Thus, the terms in the first and third brackets of equation

(7.1c) are both equal to:

m m m
1 x x2 x

which is equal to zero. Make these substitutions into equa-

tion (7.1c) to obtain:

2cov(rl,rl)xm + 2cov(rl,r2)x2 + ... + 2cov(rl,rn)x

-2cov(r,rl)xm 2cov(ro,r)x -...- 2cov(r,rn)xm

P kk
[E(rl) E(r )] Z w X = 0. (7.1d)

Define Am = wkk Thus Xm is a weighted average
of the individuals' Lagrange multipliers, where the weights

are proportions of aggregate wealth. By substituting Xm

into equation (7.1d) and collecting terms, we get:

n n m m
2 Z x cov(rl,r.) 2 Z x cov(r ,r) [E(r) E(ro)]
j=l j j=l 3 o j 1 0

= 0 (7.1e)

Now divide (7.1e) by 2 and rearrange terms to get:

n n m
E x.cov(rl,r) x.cov(r ,r = [E(r) E(r )]
j=l i' j=l 3 J 2 1o "


Equation (7.1f) is the result of aggregating the first

of the first order conditions across all investors. The

other first order conditions may be similarly aggregated

to obtain the following equation system:

n m n m m
Z x.cov(r ,r.) x.cov(r r) [E(r)-E(r)]
j=1 1 I j= 1 2 0
n m n m
Z x cov(r ,rj) Z x.cov(r rj) [E(r )-E(r )];
j=12 Ij=l o3 2 2

x .cov(r ,r )
j=l n

In order to

n m A
- xjcov(r,r) = --[E(r )-E(r,)],
j=1l 2 o

complete the proof the reader should


recognize the following relations:

n n
m m
Z x.cov(ri,r) = cov(r., Z x.r ) = cov(r ,r )
j=l j=l 3 m


m m m
where r = x1r + x 2r + ... + x r the return on the

market portfolio (of risky assets only).

Substitute these relations into the aggregate equations

(8.1) through (8.n) to obtain the following equation


cov(rl,rm) cov(ro,rm) = --[E(rl) E(ro)];
cov(r2,r ) cov(rorm) --[E(r2 E(r )];
2) r m 2 2o)

cov(rh,r ) cov(ro,r ) [E(r ) E(r )].
n m o m 2 o






To eliminate the unknown Am/2 factor from the equation

system, first multiply (10.1) by x then (10.2) by x, and

so forth.


These multiplications result in:

,r) xmcov(ro,r) = E(r x o
m 1 0 m 2 1 1 E(1

x2E(r )];

xm cov(rr
2 2 rm),r, x 2 cov~ro~rm)

2 [x2E(r2)

m ov(rnrm) m m
xcv(r ,r) xmcov(r,rm 2 [XnE(rn) xnE(ro )

Add the n equations (11.1) through (11.n) together to


n n
Z x.cov(r,r ) cov(r ,r ) i x.
i=l i i o m

m n mn m
-[ x.E(ri) E(r ) E xi
2 i= 1 1 i=

Using equation (9) and the fact that Z x. = 1,
i=l 1
equation (12) may be simplified to:

cov(r ,r) cov(r ) -[E(r ) E(r )]
m m o m 2 m o




Since cov(rm,rm) in equation (13) is simply am, the

variance of the market portfolio, equation (13) may be

rearranged to yield:

-A U2 cov(ro,rm)
= m14)
2 E(rm) E(ro)

Now substitute the results for Am/2 in equation (14)

into each of the equations (11.1) through (11l.n). For any

security, i, the market equilibrium relation for returns is:

2 -cov(r ,rm)
cov(ricov( ro,,rm) = m )o r E(r )-E(r ) (15)
which can be rearranged into more familiar form:

cov(ri,r ) cov(ro,r )
E(r) = E(ro) + cov( [E(r )-E(ro)].(16)
a cov(r rm)
m m

Equation (16) represents the market equilibrium

relation that must hold for all securities if a single

option and n risky assets (but no riskless security) are

available for trading by investors.

Equilibrium with One Option and Unrestricted Trading
in the Riskless Security

In this section a riskless security is introduced into

the framework. It is assumed that there are no constraints

on the level of holding of the riskless security for any

investor. Therefore, equations (1), (2) and (3) hold in

full. From equation (1) it is known that for any investor:

x = 1 xi x. (17)
o i=1 f

Substitute the relation above into equations (2) and (3),

the expressions for an investor's portfolio mean and

variance, to obtain:

n n
E (r) = x E(r ) + (1 x xf)E(r ) + xfrf; (18)
i=l 1 i=l o

2 n n n 2
a Z E xixcov(ri,rj) + (1 x Xf) o
r2 i=lj=l i=l o (19)
n n
+ 2(1 E x. -Xf) Z x.cov(r ,ri).
i=l i=l

Thus the investor's portfolio problem of minimizing

variance for each level of return can be solved by mini-

mizing the following Lagrange function:

2 n n
L = + [E2 (r) Z x E(r) (1- Z xi- )E(r )-x fr
L2 i=l i= 1-f


Substitute expression (19) into expression (20).

Differentiating L2 in (20) with respect to each of the port-

folio weights of the first n risky securities and setting

the derivatives equal to zero, we get:

dL2 n n 2
2[ E x.cov(r,rj) (1- Z x -xp)
dxI j= i=l

(1- Z xi-xf)cov(ro,r)

i x.cov(r rj)]
j=1J J

- X[E(rl) E(r)] = 0;

n n 2
S2[ Z x.cov(r2r) (1- x-x ) +
j=1 i=l f

n n
(1- x,-xf)cov(r r ) E x.cov(r ,r.)]
i=l I o 2 j=1 3 o

- X[E(r2) E(ro)] = 0;

dL n n 2
-2= 2[ x.cov(r,r) (1- x.-x) +
dx j=1 i=l f 0
n n
(1- Z xi-x )cov(r ,r ) Z x.cov(r ,r.)]
i=l n j= J

- X[E(rn) E(ro)] = 0.






Next take the derivative of L2 in equation (20) with

respect to xf and set equal to zero to get:

dL n 2 n
- 2= -2[(1- Z x -x )a + Z x cov(r ,r )] + A[E(r )-rJ =0.
dxf i=l 1 f o i=l o


Each investor has an equation set (21.1) through (21.n)

and (22). Each equation in the set may be aggregated over

all investors to obtain the following aggregate demand


n m n m m 2 n m m
Z x.cov(r ,r) (- Z x.-x )a + (1- Z x )cov(r ,r)
j=l J j i=l 1 f o i=l 1 f 1
j=1 i=1 i=1

n n n
-jZ x-cov(r,rj) = r -[E(rl)-E(ro)]'

n m nm m 2 n m
Z x cov(r ,r) (1- x f-x )o + (1- Z x. x)cov(r ,r)
j=l 2 j i=l i f 0 i=l f o 2
n m m
Z x cov(r ,rj) = -[E(r2)-E(ro)];
j=1 J 2

n m m m 2 n m m
Z x.cov(r ,r ) (1- Z x -xf ) + (1- x. x )cov(rr n)
j=1 J n J i=l1 i=l 1 f n
Z xcov(r ,r.) = -E(r )-E(ro)]
j=l J 1 2

where xm is the weighted sum of the individual investors'
1 p
proportions for the ith stock (i.e., x. = wkxk, where
1 k=l 1
wk is the kth investor's proportion of total market wealth).

Thus x. is the proportion of the wealth of the market port-
folio represented by the ith asset. The term xm is a

similarly weighted sum of the individual investors' propor-

tions for the riskless security.

By the use of a similar technique, equation (22) may be

aggregated over all investors to obtain:

n n m m
(1- Z xi-x)m 2 + E xcov(r,r) = -[E(r )-r (24)
i=l i=l 2

Of course, xf = 1 Z xi = 0. Using that fact, along
with equation (24), in simplifying equations (23.1) through

(23.n) we get:
n m n m
j x cov(rl,r.) i xcov(ro,r ) [E(r)-E(r )
j=1 1 2 1

n n m
Z x.cov(r r ) Z x.cov(ro,r ) = -[E(r )-E(r )]
j=l J j=l J j 2 2


n m n m
Sx-cov(r ,r.) E x.cov(r ,r ) [E(r )-E(r )];
j=1 n j=l o j 2 n


nm A
Z x.cov(r ,r) = --[E(r )-r f]
i=1 1 o 1 2


Now substitute equation (26) into each of the equations

of the (25.1) to (25.n) set to get:

n m
E x cov(rl,r.)
j=1 J

n m
E x.cov(r2,r )
j=l J


+ -[rf-E(ro)

+ -[ff-.(roJ]

= [E(rl) -E (r)]


n m
E x.cov(r ,r.) + --rf -E(r )] -[E(r )-E(r )];
j=1 3 n 2fo 2n


The equation set (27.1) through (27.n) may now be

easily rearranged to obtain:

j x.cov(r ,rj)

n m
j x.cov(r ,r.)
j=l J J

= [E(rl)-rf]

= 2[E(r2)-r ];


n m A
E x.cov(r ,r.) = [E(rn)-rfj.
j=1 3 n 2 n







It may be seen from the equation set (28.1) through

(28.n) that no traces of the option remain in the first

order conditions. Equations (28.1) through (28.n) may be

solved in a manner similar to the familiar Sharpe-Lintner

capital asset pricing model that has no options. To do

this first recall equation (9). Thus, from equations (28.1)

through (28.n):

cov(rl,rm) = L[E(rl) r ]; (29.1)

n 2 f
cov(r2,r m f[E(r2) r^]; (29.2)

cov(rn9 ) 2 [E(r^ rJf. (29.n)

Now to eliminate the Xm/2 factor from the equation sys-
m m
tem, first multiply (29.1) by xl, (29.2) by x2, and so

forth. These multiplications result in:
m X m m
xlcov(rl,rm) = i xlE(r x1rf]; (30.1)
m 1 m m
x2cov(r2,rm) = [x2E(r2) x2r ]; (30.2)

m 1 -m m
xncov(rn,rm) = nE(rn) xnrf]. (30.n)


Next sum equations (30.1) through (30.n) vertically to


n m m n nm
E x cov(r,r ) -I E(r.) r E x (31)
i=l 1 1 m i=l 1 1 f

Using equation (9) again and noting that E x. = 1
i=l 1
(since xf=O), equation (31) is equivalent to:

cov(rm ,r) = [E(r ) rf] (31a)

Note that cov(rm,rm) in equation (31a) is simply am,

the variance of the market portfolio, it follows easily

Xm a2
m *(31b)
2 E(rm)-rf

Now substitute from equation (31b)into equation system

(27.1) through (27.n), and after rearranging, the familiar

Sharpe-Lintner capital asset pricing model results:

cov(ri,r )
E(ri) = rf + 1 [E(r )-r ]. (32)
1 f 2 m f

Thus the familiar Sharpe-Lintner model applies even in

a world with an option, as long as a riskless security is

available for unrestricted trading.

Equilibrium with One Option and a Margin Constraint6
on Riskless Borrowing

In the case to be derived here xf is constrained to be

above some constant negative amount, denoted C. For

example, if C is -.50, then an investor may borrow up to 1/3

of the value of his portfolio of risky securities. If C =

1, then fully one-half of the risky asset portfolio may be

financed with borrowed funds, and so on. In this case equa-

tions (18) and (19) again represent the investor's portfolio

expected return and variance; they are repeated here as

equations (33) and (34) below, with the margin constraint


n n
E3(r) = x.E(r ) + (1 Z -x E(r ) + xfrf; (33)
i=l i f) r

2 n n n 22
a = i x.xjcov(ri,rj) + (1- i x-x f) ao
r3 i=lj=l 1 1 i
n n
+ 2(1- Z xi-x ) x.cov(r ,ri);
i=l i=l 1 o

C < xf. (35)

Thus the investor's portfolio problem of minimizing

variance for every level of expected return, given the mar-

gin constraint in (35), can be solved by minimizing the

following Lagrange function:

2 n n
L = a + [E 3(r) x E(r ) (1- E x -x )E(r )
3 i i=l f o
1=1 i=l
xfrf] + X[C x ],

where the second Lagrange multiplier, X1, relates to the

inequality constraint.

Now substitute the portfolio variance expression from

(35) into equation (36) to get:

n n n 2
L3 =Z Z xx cov(r ,r) + (1 x.-x ) a +
i=lj=l 1 J i=l 1

n n n
2(1- Z xi-x ) xicov(r ,ri) + X[E3(r)- xiE(ri)
i=l i=l

-(1- Z x xf)E(ro) xfrf] + 1[C xf]. (37)

Differentiate L3 in (37) with respect to each of the

portfolio weights of the n risky assets, and set the

derivatives equal to zero to obtain equations (38.1)

through (38.n) below:

dL n
3 2[ E x cov(r ,r.)
dx j=l 1 3

(1- C x-x )a +
i=l 0

(1- Z x -x coverr r )
i=1 1 f o 1

Sx.cov(r o r.)] -
j=1j 3 0

X[E(rl) E(ro)] = 0;

dL3 n n
= 2[ Z x.cov(r r ) (1- xi-xf ) +
dx2 j=l 2 2 0 i=l o

n n
(1- x.-xf)cov(r,r2) x.cov(r ,r)]
i=l 1j=l 3

[E(r2) E(ro)] = 0;

dL3 n n 2
-2[ Z x.cov(r ,r) (1- E x.-x )o +
dx j=l 3 n ji=l o

n n
(1- Z x-x )cov(r ,r) xcov(r ,r )]
i=l i f) n j=1 (r 0

X[E(rn) E(ro)] = 0.




Next take the derivative of L3 in (37) with respect to

xf and set it equal to zero:

dL n n
= -2[(1- Z x.-x )2 + E x.cov(r r)] +
dxf i=l i 1 o' i
[E(ro) rf] 1 = 0.

Equation (39) is different from its counterpart equa-

tion, (22), in the second section, because of the constraint

and the appearance of X.
Together with the generalized Kuhn-Tucker conditions

and equations (40) and (41) below, equations (38.1) through

(38.n) and (39) form the first order conditions to be

satisfied if there is a solution to the investor's decision


A (C x ) = 0; (40)

A1 > 0. (41)

From the complementary slackness condition, (equation
(40)), it is obvious that either A = 0 or xf = C, or both.

If XA = 0 and xf i C, then the margin constraint, while it

is publicized, is not effectively binding the investor's
decision; if A i 0 and x = C, then the margin constraint

is effective.

Aggregate each of conditions (38.1) through (38.n) and

(39) across all investors (in a manner similar to that of

the first section) to obtain equations (42.1) through (42.n)

and (43) below:

n m n n
Sx cov(r,r) (1- -x xm) + (1- x -x)cov(rr)
j=l J 3 i=l i xf o 0 i=l i (ro'

n mm
xmcov(r ,r ) = [E )-E(r )] (42.1)
j=1 6 5 2 1 0

n n n
Sx cov( r ) (1- x-x )2 + (1- m x -xm)cov(rr
j=l J 2' i=l 1 f i=o i 0 o2

x.cov(r,' ) = x- [E(r2)-E(ro)]; (42.2)
j=j2 0

n m n m m 2 n
j xjcov(r ,rj) (1- ) x.-x )o + (1- i xi-xf )cov(ro,rn)
j=l n j i=l 1 i=l

n m
m X
x.cov(r ,r ) = [E(r )-E(r )]; (42.n)
j=1 o J (ro

n m n m m
-(1- x -xf)a E x.cov(r r ) = [rf-E(r )]+ x1 ,(43)
i=l i=l 2

m m Im
where x. and xf are as before, and X is the weighted
aggregate of all the individual investors' X 's divided by
1m 1 P k Ik 1k
two, i.e., Z w X where X is the kth investor's

1 k
X and w is the proportion of market wealth held by the

kth investor.
If X is equal to zero, i.e., if the margin ceiling is

not restrictive on anyone, then equation (43) reduces to

equation (24). Under that circumstance the problem is no

different than the case of unrestricted trading in the risk-
less security. If X is not equal to zero, then the margin

constraint is binding on at least one investor. In this

case the path to establishing equilibrium relations from

equations (42.1) through (42.n) and (43) is different than

if Xlm = 0. To continue, assuming Xlm / 0, first recognize

that x = 0. Consequently, the aggregate amount of the

riskless security is assumed to be zero in the same manner

as the option.
mn m
Using the fact that x = 0 = 1 E x. and using equa-
i=l 1
tion (9), equations (42.1) through (42.n) and (43) may be

reexpressed as:
cov(rl,rm) cov(r0,r) = 2[E(rl) E(ro)]; (44.1)
cov(r ,r ) cov(r ,r) = -[E(r) E(r )]; (44.2)

cov(r ,r ) cov(r,r ) [E(r ) E(r )]; (44.n)
cov(rn,rm) cov(ro r ) = [E(r ) E(r )]; (44.n)
0 mm 2 n o
cov(ro,rm) = 2[E(ro) rf] X (45)

Equation set (44.1) through (44.n) and (45) must hold

in market equilibrium. Equation (45) is the relation

between the equilibrium expected rate of return on the

option and the riskless rate. Equations (44.1) through

(44.n) may be used, ignoring (45), to derive the same

equilibrium conditions as those in the first section of the

chapter. Equation (16) would hold in the case just des-

cribed, since:

m 2 cov(r m) (46)

2 E(r ) E(ro)

Substitute equations (46) and (45) into the ith equa-

tion of (44) to get another equilibrium expression for

the margin ceiling case, equation (47) below:

lm m cov(r ,rm)
cov(ri,rm) + A = m [E(ri)-f]. (47)
E(r ) E(r )

By rearranging equation (47) it is found that:

cov(ri,r ) + X
E(r.) = rf + m[E(r )-E(r )]. (48)
S2 cov(r ,r m 0
m o 'm

Consider now equation (48) applied to a zero beta


E(rc) = rf + 2 [E(rm)-E(r)]. (49)
am cov(r ,rm)

If one assumes that options are used by many investors in

lieu of margined stock, then an obvious connection exists

between an increase in the number of options being traded and

a decrease in A As is evident from equation (49), a de-
crease in X should create a decrease in E(r ). This

establishes the dissertation's hypothesis that:

As the quantity of options being traded increases,

the equilibrium expected zero beta rate of return


The model in (49) and this chapter is not sufficiently

detailed in assumptions enough for one to establish a precise

mathematical relation between the number of options in the

option market and the aggregate margin constraint multiplier,
A Perhaps a direct link could be established under some

assumptions involving investor heterogeneity, but this task

is not undertaken at the present time.


All investors are assumed to have perfect information.

The no-inflation assumption is relaxed in some CAPM's
(e.g., Solnik's [1978]), but such models are beyond the scope
of this study.

Alternatively, the option will eventually be viewed as
a portfolio of options.

4The interpretation of Xk is that Xk is the amount of
additional expected return the investor must get if he is
to accept a small amount of additional variance in his port-

The superscript k has been dropped for convenience. The
proofs in this section and the next are abbreviated somewhat
from the detail of the previous section.

Black (1972) and Vasicek (1971) previously provided re-
sults in this area. Black considered no riskless borrowing
but allowed riskless lending, a circumstance that has been
assumed here to be impossible. Vasicek looked at the margin
constraint idea, but assumed the riskless rate to be zero.

7ee Lueberger (1973)
See Luenberger (1973).



The literature about the effects of new option trading

includes both theoretical as well as empirical papers. The

theoretical contributions have been made in frameworks other

than the one employed in Chapter II; however, the author is

not aware of any other work that formally considers the

role of any kind of futures contract, let alone options

specifically, in the single-period mean-variance framework.

Since the other theoretical studies employ other frameworks,

a detailed review of those studies is omitted here. The

interested reader is referred to the works of Hirshleifer

(1975), Danthine (1978), Ross (1976), Schrems (1973),

Townsend (1978), Breeden (1978), Rubinstein (1976a), Friesen

(1979), and Long (1974). The roles of futures (and

especially options) identified by the above theoreticians

do vary depending upon which framework is employed. While

the papers are interesting, they are too complex to adequately

review here.

On the empirical side attention has been focused on the

effects of options on the underlying securities, rather than

on market-based variables. In particular, no study has been

concerned with the impact of options on market factor interest

rates. Despite this, several of these studies are reviewed

in this chapter, since the studies do contain evidence that

is interesting in light of the present topic and methodology.

There are two categories of empirical studies associated

with effects of options. The first category concerns the

short-run effects of option expirations. The second category

concerns the effects of the advent of new option trading in

the long-run as well as the short-run. Only the second cate-

gory of these studies is of direct interest to review here.

Klemkosky and Maness

In a study published in 1980 by Klemkosky and Maness

(K-M), the results of an extensive investigation of the

impacts of new option trading on the underlying stocks were

reported. The major conclusions at which K-M arrived were:

(a) that the options had a negligible impact on the risk of

the underlying stocks; and (b) that excess returns which

had existed in underlying stocks before the commencement of

new option trading had been bid out of the stock prices

subsequent to the option listing.

The K-M study examined two risk measures for the under-

lying stocks, beta and standard deviation, and one performance

measure, Jensen's (1969) alpha (c). The K-M methodology of

measuring alphas, betas, and changes in alphas and betas is

basically the same as one portion of the methodology proposed

in the next chapter: the Gujarati (1970) interactive dummy

variable technique applied to the excess returns version of

the linear market model. In the form shown by K-M, the

linear excess returns market model is given below in equation


it t = :i + i(rt
r.t rft 1 rmt

- rf) + eit

ri = the monthly holding period return, including
dividends as well as price appreciation of
security i in month t.

rft = the 30-day T-bill yield on a bond equivalent
basis in month t.

rmt = the CRSP Investment Performance Index, including
dividends, in month t.

= the intercept term representing Jensen's
performance measure.

6i = beta of security i, and
eit = a random error term.

To the above market model, K-M applied the Gujarati

interactive dummy variable technique using time series data.

Thus K-M estimated the parameters in the following model:

rit rft = + D + 6i (r rft) +
it f i mt rt-

f3 (rm
i mt

- ft) D + eit
ft it



D = 0 for the period subsequent to the option
listing ("post-listing")

D = 1 for the period prior to the option listing
Thus .i and Bi are measures of the post-period alpha and beta,
1 1
while + and B + B are measures of the pre-period

alpha and beta.


The K-M study analyzed three "waves" of stocks that be-

came underlying securities for options. Group 1 stocks

consisted of the 32 stocks which had options listed from

April 1973 through October 1973. (These were the stocks

from which came the ones used as underlying stocks for this

study.) The pre-listing period data for Group 1 consisted

of monthly security returns for the 36 months from January

1970 through December 1972. The post-listing period was

from January 1974 through December 1976. K-M dropped the

1973 period so as to avoid any problems in testing that

might be associated with the effects of the announcement

of option listing on the underlying securities.

Group 2 stocks consisted of the 32 securities which had

options listed on the CBOE beginning December 1974 and ending

June 1975. (No new options were listed between October 1973

and December 1974, but many were listed continuously after

June 1975.) K-M omitted December 1974 through June 1975

from their analysis of Group 2 stocks. The pre-listing

period was January 1971 through November 1974 for Group 2.

The post-listing period was July 1975 through June 1978.

Group 3 consisted of the 39 stocks that had options

listed on the American Stock Exchange CASE) from January 1974

through June 1975. This time period was omitted from the

analysis. The pre-ASE period was from January 1972 to

December 1974, and the post-ASE period was from July 1975

to June 1978.

K-M utilized two different market indices--the CRSP

equal-weighted (EW) and the CRSP market value-weighted (VW)

indices. K-M noted that the 103 securities, because of their

large market values, will dominate or greatly influence in

the aggregate any market value-weighted index, so the authors

used both indices. The empirical results of the K-M study

are summarized next.

K-M observed that the performance measure (=) decreased

for most securities in the post-listing period (83 out of 103

for the EW index and 68 out of 103 for the VW index). This

result was also observed for the "portfolio of all underlying

stocks" in each group. In all 6 cases the performance measure

dropped; 5 of the 6 cases were significant. K-M also noted

that the performance measure had been significantly positive

in 5 of the pre-listing cases, and not significantly dif-

ferent from zero in any of the post-listing periods. K-M

concluded that excess returns appeared to have been bid out

of the underlying security returns with the advent of option

trading. This conclusion is somewhat consistent with the

theory and hypothesis of this dissertation; however, a major

caveat for the K-M study is the Roll (1977a) critique.1

The changes in the betas (measures of systematic risk)

for the stocks were not so consistent. Only a few stocks

had significant beta changes. More stocks showed beta de-

clines than increases. Viewing the stocks in each group as

portfolios the beta changes were also not consistent: the

portfolio beta for the Groups 1 and 2 stocks declined, and

the Group 3 portfolio beta increased. Anyway, the changes

were insignificant in all 6 cases.

K-M also observed that the coefficient of determination,

R2, did not change significantly from the pre- to the post-
listing periods. The R was calculated as the proportion

of the total variation of stock returns explained by the

linear relationship with the market portfolio.

K-M finally looked at changes in total risk (measured

by variance) for the stocks and the portfolios. They found

that the total risk of the Group 1 stocks went up after

option listing, while the total risk of the Groups 2 and 3

stocks went down in the post-option listing period. In-

terestingly, the variance of the market index behaved in

the same direction. The change in the variance of the mar-

ket index and in a large proportion of the stocks was


The changes in the variances of the portfolios were

not consistent. The Group 1 portfolio variance increased,

but not significantly. The Group 2 and Group 3 portfolios

experienced a decline in variance; the decline was significant

for Group 2, but not for Group 3. Also K-M reported the

variance comparisons using "deflated" returns. Deflated

returns were defined to be (rit rft) divided by (rmt rft)

and were used to account directly for shifts in the market

index variability of returns. When the alternative method

was used the results for the individual stocks were about

the same. However, the change in portfolio variance for

Group 1 became significant in the case of the EW index. The

change in the variance of the Group 2 stocks switched from

being significant to insignificant. The Group 3 portfolio

variance changes switched from being insignificant to

significant, but again only in the case of the EW index.

This concludes the review of the Klemkosky-Maness


Hayes and Tennenbaum

A study that was conducted by Hayes and Tennenbaum (H-T)

was published in 1979, and it analyzed the impact of option

trading on the volume of trading in the underlying shares.

The authors' statistical tests indicated that an effect of

listed options was to increase the volume of trading in the

underlying shares. H-T theorized that this effect occurred

because the availability of the options increased the number

of ways that the underlying stock can be used in investors'


H-T conducted 2 different types of tests. The first

was a cross-sectional analysis. H-T compared a 43-company

sample of optioned stocks with a control group of 21 stocks

of similar size, but for which there were no options. Using

a system of dummy variables for the option group and the

control group, and for a pre-option trading period and a

post-option trading period, H-T analyzed the percentage

trading volume compared to the total NYSE volume. H-T found

that the control group's trading volume was about 17% of the

NYSE total in the pre- and post-option periods. (The pre-

option period was May 1972 to April 1973; the post-option

period was May 1973 to September 1977.) H-T also found that

the optioned stocks had a mean percentage volume of 25% in

the pre-option period and that the percentage jumped to

almost 34% in the post-option period.

The second analysis that H-T performed was a longitudinal

analysis. The authors examined the stock volume for at least

a year before and a year after options began trading on that

stock. For the 43 companies, stock volume was the dependent

variable in a multiple regression on the 2 independent

variables: option volume and NYSE volume. In 1 version of

the longitudinal test the 43 stocks' volume data were

aggregated, and so was the option volume data. In the

second version of the longitudinal test the stocks' volumes

and option volumes were analyzed individually.

H-T found that in the first version of the longitudinal

analysis there was a significant association between stock

trading volume and option trading volume. The results of

the second version, the longitudinal test with each indi-

vidual stock, showed corroborating results.

Hayes and Tennenbaum concluded that they had provided

evidence of a linkage between option trading and price

"continuity" in the underlying shares. That is, H-T linked

the volume increases with price continuity in the underlying

stock. This is an interesting finding and appears to be

evidence that options improve market efficiency, at least

for the underlying stocks. Since increased volume of under-

lying stock trading has not been predicted by this disserta-

tion's theory, such an increase does not contradict the

hypothesis here. Increased underlying stock trading volume

could (intuitively) be a manifestation of the relocating

process of the risk-return intercept. Further analysis of

this point here, however, is beyond the scope of this review.

Reilly and Naidu

In a paper that has been professionally presented, but

not as yet published, Reilly and Naidu added their analysis

of option trading impacts on underlying stock volume and

volatility to the existing evidence. In addition, Reilly

and Naidu (R-N) examined the impact of option trading on

the market liquidity of the underlying stocks. R-N employed

2 types of measures of liquidity. The first was the bid-ask

spread; the second was a version of the Amivest Liquidity


The Amivest Liquidity Index attempts to relate the

average dollar amount of trading to a 1% change in the price

as follows:
E Pt Vt
Amivest Index. = t(3
1 n ( '
E %APt


Pt = Closing price for stock i, on day t.

Vt = Share volume of trading for stock i on day t.

%APt = The percent change in price for stock i on day t.

The higher the dollar volume of trading is, per 1% of price

change, the higher will be the liquidity of the stock, in

the opinion of the users of the Amivest Index. Reilly and

Naidu modified the Amivest Index so that inter-day price

ranges were accounted for as follows:
Modified Liquidity Index = t (4)
n fH-L
SH+L/2 t


H is the high price for the day, and

L is the low price for the day.

The R-N analysis focused on effects surrounding the

listing dates of options on the CBOE and the ASE. The 5

days before and after the listing dates were excluded.

Activity in the 20-day periods before and after the 5-day

periods were examined. In all, 12 stocks listed on the CBOE

on May 22, 1975, and May 23, 1975, and 10 stocks listed on

the ASE May 30, 1975, were tested. Also, control groups

of 12 and 10 randomly selected NYSE stocks were studied.

The results of the R-N tests for effects of options

on the market spreads indicated that the percentage spread

for the optioned stocks was lower than that for the random

stocks. In addition, in going from the pre-listing period

to the post-listing period, the market spread declined

slightly for the optioned stocks and increased slightly for

the random stocks. The change was not significant, but R-N

remarked that the market for the optioned stocks was still

superior in terms of liquidity to that for the random stocks.

With regard to both the Amivest Index and the Modified

Liquidity Index, however, R-N observed virtually no change in

market liquidity. Thus Reilly and Naidu observed no signifi-

cant changes in any measures of liquidity.

R-N used the same data in their analysis of changes in

underlying stock volatility and relative trading volume. In

addition, R-N examined an aspect of price performance of the

stocks in the 20-day periods on either side of the listing

time. Five nonsystematic volatility measures were examined

for the optioned stocks, the random stocks, and the S & P

400 Industrial Index. For all 5 measures the stock price

volatility of the optioned stocks declined in the period

after listing. The range of the decline was from 25% to

35%. The volatility of the random stocks declined by a

smaller amount, from 3% to about 8%. Relative to the market,

the volatility of the option stocks also declined in the

post-listing period. Since the R-N stocks are contained in

Klemkosky and Maness' Group 2 and Group 3 stocks, R-N's

findings are basically consistent with those of K-M, dis-

cussed earlier.

For the volume of trading, R-N found no significant

change in the relative volume of trading of either the

optioned stock groups or the random stock groups. R-N

concluded that there was almost no short-run impact on the

volume of trading for the underlying stocks as a result of

option listing. This conclusion is not necessarily con-

tradictory to the Hayes-Tennenbaum report, since H-T looked

at a much longer term.

Reilly and Naidu also looked at price performance from

the following perspective: they measured the ratio of the

average price of an optioned stock to the average price of

a random stock. In the pre-listing period the price ratio

was stable in the 1.73 to 1.80 range. In the post-listing

period this ratio jumped to 1.98 and remained in this range

for about 7 days. Then the ratio gradually declined back

to the pre-listing period range. No possible reasons were

offered for this finding.

Trennepohl and Dukes

An analysis of the effect of option listing on under-

lying stock betas was the focus of a paper, published in

1979, by Trennepohl and Dukes (T-D). T-D examined the

original 32 stocks listed on the CBOE from April 1973 to

October 1973. Weekly holding period returns on each of the

32 stocks and on each of 18 nonoptioned stocks (assumed to

be in a control group) were examined. The nonoptioned

stocks were selected as a stratified random sample, repre-

senting the same industries present in the optioned stock


The betas were calculated for all of the 32 optioned

and 18 nonoptioned stocks for a 2-1/2 year period from

October 1970 through April 1973 ("before") and a 2-1/2 year

period from October 1973 through April 1975 ("after").

The betas were calculated using the following (no

excess returns) market model:

R. = a. + b. R + e (3-5)


R = weekly holding period returns of stock i.

a. = Y-axis intercept.
Cov (Ri,RM)
bi = beta of security i, i.e. ( ar(RM) ).

RM = the rate of return on the Standard and Poors
500 Index.

e. = random error term.
The betas obtained from the regressions were analyzed

by 3 methods. The first method was a paired differences

test. The mean of all 32 optioned stock betas was 1.22

before 1973 and .873 after 1973, a mean difference of -.347.

Since the t value associated with this mean difference was

-3.574, T-D claimed that the observed change was significant.

However, a similar result was observed in connection with

the nonoptioned stocks. The average beta changed from 1.137

to .934 (a change of -.203), with a t value of 2.317. T-D

observed that it appeared that the reduction in the betas

had been caused by "general market influences" rather than

the option trading.

However, in a t-test of the difference in the mean

change of the betas, the authors found that the hypothesis

of no mean difference change, between optioned and non-

optioned stocks, could be rejected at a confidence level

slightly over 89%. A nonparametric Chi-Square test of


directional changes in the betas essentially confirmed the

results of this t-test. Thus, it was concluded by K-D that

the betas for the option stocks decreased more than the

betas for the nonoptioned stocks, but with a statistical

level of confidence that may be considered to be marginal.


There is no finding in any of the studies reviewed that

is contrary to the hypothesis of this dissertation at the

level of theory presented. In fact, the conclusions that

excess returns have been bid out of underlying stock prices
by K-M, and that trading in the underlying stocks has

become more continuous by'H-T, tend to support the theory

of this study.



Roll (1977a) called into question any studies of
empirical estimates of systematic risk obtained by time
series regressions of returns on a market index; he showed
how far off results could be if one doesn't know the "true"
market portfolio.

2Roll's caveat notwithstanding.


Excess Returns

The previous chapters established the general hypothesis

that as more options are traded, the equilibrium expected

zero systematic risk rate of return, E(r,), will theoretically

decline toward the riskless rate of interest. Since addi-

tions of new options to the market have occurred over a

period of time, the empirical analysis here should involve

an examination of time series. Specifically, time series

values of rf and rZ are to be examined and tested during

periods when new options did and did not begin trading. In

a time series analysis of riskfree and zero systematic risk

rate estimates, the possibility that rf and E(r,) could

change, for reasons other than new option trading, is a

problem. This problem may be easily overcome by focusing

the analysis on excess return; an excess return is defined

to be an observed return minus the corresponding time series
observation for rf. Let r denote the time series of

differences between each observation for r and the corres-

ponding observation for rf.

Zero Systematic Risk Returns

Neutral Spread Returns

An obvious candidate for a zero systematic risk port-

folio is a neutral option spread of the kind identified by

Galai (1977). In order to simulate the performance of a

neutral spread one must determine dCl/dS and dC2/dS, the

first derivative values of the 2 call prices with respect

to the stock price. The neutral spread is created by going

long dC2/dS times the first option and going short dCl/dS

times the second option.2 The neutral spread is riskless

for the instant during which it is created; neutral spreads

have zero systematic risk over discrete short time intervals,

assuming normally distributed underlying stock prices.3

In order to employ the neutral spread method for

simulating r returns, one must know or assume a differen-

tiable option pricing function of the underlying stock price.

Fortunately, several alternative option models are suitable

for usage in the method of neutral spreads. The Black-Scholes

(1973) model, the most popular option model in finance

research at present, is:

C = SN(dl) Xe-rN(d2)

where S Black-Scholes
In + + (r + C /2)T Model
dI (1)


d2 = d1 a T

In equation (1) C is the price of the call option; S is

the price of the underlying stock; X is the option's exercise

price; r is the continuously compounded riskfree interest rate,
a constant over time; a is the continuous variance rate, also

a constant over time; T is the time until expiration of the

option; and N(d ) is the cumulative unit normal distribution

function value at d If equation (1) is assumed as a valid

call option pricing model, then dC/dS, for use in construct-

ing neutral spreads, is N(d1).

Significantly, equation (1) holds only for options that

are dividend-protected or for options whose underlying stocks

pay no dividends.4 A dividend-protected option is one whose

exercise price automatically is adjusted, without loss or

gain in the value of the position of the call owner, for cash

dividend payments made to the holders of the underlying

stock. Since U.S. exchange-listed options are not dividend-

protected, and since underlying stocks commonly pay dividends,

some extension of equation (1) is desirable; a popular can-

didate for a nondividend-protected option model is the Merton

(1973b) model:

C = e-d- S N(d{) Xe-r N Cd2)

where Merton
In + (r d + 2/2)T Model (2)
d =


d = d' a
2 1

In equation (2) d is the continuously compounded dividend

yield, based on the current stock price, S. The Merton model

assumes d is constant and known in advance; the other vari-

ables in his model are the same as those in the Black-Scholes

model. In fact, the Merton model is the same as the Black-

Scholes model except for the dividend assumption. The first

derivative of C with respect to S, using equation (2) is:

dC/dS = e-dTNCd), (2')

which is an input necessary to the method of neutral spreads.

The interest rate, r, in either of the equations, (1)

or (2), is essentially the same concept as the riskfree rate

described in Chapter II. However, as was indicated in this

chapter, the r in the option models is a continuous-time

instantaneous riskless rate, which is assumed to be constant

over time. Of course, in reality the short term interest

rate appears to be stochastic rather than constant. However,

Merton (1973b) has argued that the continuously compounded

equivalent to the discrete-time treasury bill rate for the

next T years is suitable as an interpretation for r in

equations (1) and (2).

Four of the variables in equation (2) are directly ob-

servable; they are the stock price, S; the exercise price,

X; the time to maturity of the option, T; and the dividend

yield, d. The dividend yield is not usually known in ad-

vance with certainty, but educated forecasts will very often

be correct, since most companies follow "stable" dividend


The variable in equation (3) which is not directly ob-

servable is a Option researchers once thought this vari-

able could be estimated with reasonable accuracy using

historical data; however, Geske (1979) has discussed and

pointed out some potential inadequacies with using his-

torical variance estimates in option models. Since some

value for a2 must be assumed in order to calculate the

derivative in equation (2'), and since historical estimates

of 02 are potentially inadequate, the implied variance method

of Latane' and Rendleman (1976), Chiras and Manaster (1978),

and Trippi (1977) must be employed. The implied variance

method yields a value for a2 by the researcher (a) observing

an actual option price, (b) assuming the option model holds

true, and (c) calculating the value of a2 that equates the

option formula price to the actual option price.

Previous Empirical Results of Neutral Spreads

Empirical analyses of neutral option spread returns

for daily and monthly holding periods have been reported

by Galai (1977) and Chiras (1977), respectively. Galai,

who used the now-suspect hlis.rical variance approach, re-

ported the appearance of skewness in the frequency distri-

bution of neutral spread returns (see note 3). In addition,

Galai reported that the neutral spread returns had variances

that were too large to permit statistical inferences to be

made. c.hi-s avoided the historical variance problem by

using the implied variance method. Chiras found, on a

selected basis, some strikingly high returns; however, he

did not report any analysis of the statistical properties

of the neutral spread returns. In fact, neither Galai nor

Chiras considered the important empirical question of

whether the neutral spreads contained any systematic risk.

Neutral Hedge Returns

A second possible candidate for a model of a zero

systematic risk security is very similar to a neutral

option spread. However, rather than a neutral spread of

2 options, the second method involves a neutral hedge of

1 option and the underlying stock. A neutral hedge is

created by going long a share of the stock, and simultan-
eously going short 1/ga- options. Neutral hedges neverthe-

less have zero systematic risk under the same conditions

as neutral spreads.5

Previous Empirical Results of Neutral Hedges

Returns of neutral hedges have been examined by Black

and Scholes (1972), Galai (1977), and Finnerty (1978). None

of these 3 studies employed the adjusted-for-dividends model,

equation (2): Black and Scholes and Finnerty used equation

C1) exclusively; Galai used equation (1) primarily, and

later considered the effects of dividends by a different

means than equation (2). All 3-studies employed the proble-

matic historical variance approach. Both Black and Scholes

and Galai analyzed daily holding periods; they claimed to

have found no evidence of significant systematic risk in

their neutral hedge positions. Finnerty looked at weekly

holding periods; he claimed he did find some significant

systematic risk. These researchers' opinions are highly

regarded, and their findings are not necessarily illogical.

However, all 3 studies measured systematic risk by the

commonplace method of calculating the regression coefficient

in a least squares regression of hedge returns on a market

index, and this method may be invalid, as Roll (1977a) has


Direction of the Methodology

A choice should be made between neutral spreads and

neutral hedges. For this study neutral hedges are chosen,

because of the report by Galai that neutral spreads had

skewness and large variances. To calculate neutral hedge

returns, the method of implied variance will be used; thus,

the "historical variance problem" of the 3 previous studies

of neutral hedges will be avoided. In addition to the

improved technology in estimating 2, this study will

utilize the adjusted-for-dividends model, equation (2);

since equation (2) was not used in any of the 3 previous

studies of neutral hedges, the use of equation (2) here

represents an improvement over previous work. Finally,

since Roll has cast doubts about the meaningfulness of the

systematic risk estimates of the 3 earlier neutral hedge

studies, some effort will be made in this study to assess

systematic risk through an alternative method. The alter-

native method will be elaborated upon later in the chapter.

Two Further Considerations

1. There is a potential problem in employing neutral

hedge returns to model zero systematic risk security returns

for the purpose of testing the dissertation's hypothesis.

The problem is that the risk-return slope may shift during

the time span being studied. If the slope of the risk-

return relation were to change over the time span studied,

then there would be some movement of the underlying stock

prices to new equilibrium levels. This movement in the

underlying stock prices could cause some abnormalities in

hedge returns that may obscure the direct effect of the

option trading on ECrz). The abnormalities would most

logically be expected to have an impact on the results if

the underlying stocks used to construct the neutral hedges

in this study were imbalanced by being comprised of either

too many high systematic risk stocks or too many low sys-

tematic risk stocks. In order to account for this problem,

an empirical analysis must be employed to check for the

presence of the potential effects of a shifting risk-return

slope. The procedure is described later in the chapter.

2. The other consideration in connection with neutral

hedges is whether a portfolio may be viewed as the minimum

variance zero systematic risk portfolio in the capital mar-

ket. If investors could continuously readjust their hedge

*positions, a portfolio of neutral hedges would surely be

the minimum variance zero systematic risk portfolio, since

each of the neutral hedge returns would have no uncertainty

at all. However, the problem is that investors cannot

continuously readjust hedge positions, and so a positive

variance must be a property of neutral hedges held over dis-

crete time intervals. As has been pointed out by Boyle and

Emanuel (1980), it is possible to reduce the variance of

individual neutral hedge positions by about 3/4 by con-

structing a portfolio of a large number of neutral hedges.

This dramatic variance reduction is made possible by the

low correlation between neutral hedges relative to the

correlation between (positively correlated) underlying stock

returns. Thus an empirical question, that is important to

this research study, arises: whether the variance of the

neutral hedges can be reduced by enough through diversifi-

cation to permit us to consider a portfolio of neutral

hedges as having the minimum variance of all zero systematic
risk portfolios.89

Selection of Time Span, Holding Period, and Data

Time Span: November 30, 1973--August 29, 1975

The empirical study should extend over a period of

approximately 2 years in order that the predicted effects

of new option trading be given ample time to show up. The

21-month time span from the end of November, 1973, through

August, 1975, was selected. This time span is divided into

2 contiguous segments. The first segment, Period 1, is the

12-month term ending November, 1974; during this segment no

options began trading on any new underlying stocks. The

second segment, Period 2, is the 9-month segment beginning

at the end of November, 1974; during this time options began

trading on a total of 82 new underlying stocks. Table 4-1

shows the frequency distribution for the number of new under-

lying stocks over the months of both Period 1 and Period 2.

Holding Period Assumption: Monthly Observations

The next decision is the assumed holding period for

which to simulate returns. A holding period of longer than

1 month, given the 21-month span of the study, would not be

feasible, because too few time series observations would

result to perform any meaningful statistical analysis. For

a shorter holding period, 2 weeks or 1 week, more time

series observations would lie within the time span; in

addition, there should be less liability of systematic risk

in the neutral hedge returns.

Although these arguments in favor of shorter holding

periods are reasonable, given the time span, holding periods

shorter than 1 month would entail high data gathering costs.

To see why the data costs would be so high under those

circumstances, consider the portion of the study concerned

with the reduction of hedge variance via naive diversifi-

cation. To facilitate the best possible naive diversifi-

cation analysis it is necessary to use the price observations

of all options quoted at any one point in time. The ob-

servation of all options at the end of each of the 21 months

in the time-span will result in 2985 usable neutral hedge

returns for the study. Since all of the data must be gathered

Frequency Distribution of
New Underlying Stocks
December 1973 -- August 1975

Month Number of New Underlying Stocks

1. December 1973 0
2. January 1974 0
3. February 1974 0
4. March 1974 0
5. April 1974 0
6 6. May 1974 0
7. June 1974 0
8 July 1974 0
S9. August 1974 0
10. September 1974 0
11. October 1974 0
12. November 1974 0

13. December 1974 8
14. January 1975 20
15. February 1975 0
16. March 1975 6
17. April 1975 0
.18. May 1975 12
S19. June 1975 31
- 20. July 1975 5
21. August 1975 0

Total Added In Period 2 82

by hand, the use of weekly or even bi-weekly data would

significantly increase the data costs over those for monthly

holding periods. Thus, monthly holding periods have been

chosen. The problem of potential systematic risk in the

hedge returns should be addressed, since some possibility

exists that monthly holding periods are not "short enough"

(in the Black-Scholes sense) to validate the assumption of

zero systematic risk.

Description of the Data

All available options at the end of each month from

November, 1973, through August, 1975, are employed in this

study. For this study an option is "available," if it

satisfies the following criteria: (a) The option's under-

lying stock must have been on the CBOE's list of underlying

stocks throughout the entire 21-month time span; (b) two

consecutive month-end price quotations must have been ob-

servable; (c) neither month-end price observation was

allowed to be below the option's intrinsic value at that

point in time, with intrinsic value defined as:

I = S + D X


I = Intrinsic value

S = Stock price

D = Dividend of the stock over the life of the option

X = Exercise price of the option.

Options whose observed prices are below their intrinsic

values are excluded from the study, because their price

observations obviously violate the logic that riskless

arbitrage opportunities have already been eliminated by

professional traders.10

Only 32 stocks comprised the list of underlying stocks

as of November 30, 1973. (This remained the entire list

until new options began being added in Period 2.) The

options of 2 of these stocks, Great Western Financial, and

Gulf and Western, were not used in the study, due to data

gathering complications in both cases. The list of the 30

underlying stocks used in the study is given in Table.4-2.

For various reasons the number of available options

will vary from month to month; therefore, the number of

neutral hedges from which to calculate r values will vary

as well. This situation must and will be considered in the

statistical analysis.

The option closing prices were observed for each month

from the Wall Street Journal at the beginning of the fol-

lowing month. Thus, the first-of-the-month Wall Street

Journals were consulted from December 1, 1973, through

September 1, 1975. Stock prices were also observed as

monthly closing quotes from the same Wall Street Journals.

Dividends were obtained from Moody's Dividend Record for

the period; these amounts were first converted into con-

tinuously compounded dividend yields and then employed in

equation (2') for the computation of the "hedge ratios."ll

Two proxies for the riskless rate are used in the study.

For use in equation (2'), r was assumed to be the mid-point

Underlying Stocks Used in the Study

Atlantic Richfield
Bethlehem Steel
Eastman Kodak
International Harvester
NW Airlines

Sperry Rand
Texas Instruments
Kerr McGee

of the two continuous rates of interest implied by the bid

and ask prices of U.S. Government treasury bills maturing

at the (approximate) time the option expires. For use in

the time series hypothesis test, rf is the midpoint of the

2 one-month rates of interest implied by the bid and ask

prices of treasury bills with 1 month to maturity. Thus

for each month t, 1 observation for rft is available. The

observations for r and rf are from the same Wall Street

Journals as the option prices and stock prices.

Preliminary Procedure

For each month, t, the number of available options is

necessarily the number of different neutral hedge position

returns, Nt. For example, in month 1, the number of options

and thus hedge returns, is 119. The number of "available"

neutral hedge positions in each of the 21 months of the

study is provided in Table 4-3. From Table 4-3 one can see

that 1381 total observations are included in Period 1, and

1604 in Period 2, for a combined total of 2985. Since the

returns in any month are representative of no more than 30

underlying stocks, there is often more than 1 hedge return

for each of the underlying stocks for each month.

Neutral Hedge Returns

Consider the jth neutral hedge for month t, out of the

total of Nt available neutral hedges for that month. Let

Sjt represent the price at the beginning of month t of the
underlying stock of the jthneutral hedge. Similarly, C
underlying stock of the j neutral hedge. Similarly, C.

The Number of Options Employed in the


Month Number of Options
Number Month Employed in the Study

1 December 1973 119
2 January 1974 116
3 February 1974 115
4 March 1974 157
5 April 1974 112
6 May 1974 110
7 June 1974 133
8 July 1974 97
9 August 1974 137
10 September 1974 163
11 October 1974 125
12 November 1974 197
13 December 1974 201
14 January 1975 127
15 February 1975 156
16 March 1975 182
17 April 1975 126
18 May 1975 159
19 June 1975 136
20 July 1975 136
21 August 1975 181

Total 2,985

denotes the price at the beginning of month t of the option
of the j neutral hedge of month t. The associated hedge

ratio, defined from equation (2'), is 1/(e-d N(dj)). The

corresponding ending prices of the stock and the option are

denoted as Sjt+ and Cjt+1, respectively. The realized

return on the jth neutral hedge over month t is thus given

by: S 1 (C C.)
jt+l Sjt -dT jt+ Cjt
Xjt= N(d)jt (4)
1 C.
t e-d *.N(di)jt t

The denominator in equation (4) is the amount of equity

assumed to be invested in the jth neutral hedge at the be-

ginning of month t. The numerator in (4) represents the

change in the dollar position of the hedge from the beginning

of the month to the end of the month.

Naive Diversification

From the Nt options in period t, portfolios of neutral

hedges of size 5, 10, 15, 20, 25, 30, 35, 40, 45 and 50 will

be examined. Portfolios of the various sizes will be con-

structed for each month of the study; for any of these port-

folios the neutral hedges to be included will be randomly

selected (with replacement). First, the computer selects a

random number representing one of the 30 stocks; next,

another random number determines which of the options to

choose for that stock. Then another stock is chosen randomly,

and so on. This process is repeated until the desired number

of neutral hedges has been randomly drawn from the Nt

available positions. The portfolio return is calculated as

an arithmetic average of the returns on each of the neutral

hedges assumed to be in the portfolio. Therefore, equal

amounts of invested equity are simulated in each of the

neutral hedges of the portfolio.

For each portfolio size for each month, 30 portfolios

will be constructed according to the random selection method

described above. The variance of the 30 portfolio returns

will serve as an estimate of the portfolio variance of a

naively-diversified portfolio of that number of neutral

hedges. 'Although the simulated portfolios could easily

contain some of the same hedge positions, the returns of

each of the 30 portfolios for a given size will be assumed

to be independent. Thus, the variance of the 30 portfolio

returns for a given portfolio size serves as the variance

estimate of a portfolio of that number of neutral hedges.

The results of this naive diversification analysis are

reported in the next chapter.

The Hypothesis Test

Preliminary Test Procedure

Let each of the neutral hedge returns be transformed

into "excess return" form--define Xe as

X = Xt r (5)
jt jt ft

for all j in the month, and for every month, t. For any

month t, j may take on a value from 1 to Nt.

Let PI stand for Period 1 and P2 stand for Period 2.

The hypothesis test is a test for the difference between
e e
the mean of the Xe values in P1 and the mean of the Xe
jt jt
values in P2. Let U stand for the mean of the Xe in P2.
"1 jt
To test the hypothesis, specify the following relationship:

Xe P1 = U + e. (6)
jt 1 j

Xe P2 = U + ej (7)
jt 2 e
where e is an error term.

In order to test the hypothesis, the mean return in

each month t must be calculated as follows:

e = EX N (8)
t j=t t

If (a) the Nt are equal for all t; (b) the standard
deviations around the X are equal for all t (that is, if
(Xe t e 2

S jl jt
SN -1

are equal for all t); and (c) there is no systematic risk in

the neutral hedges, then the test procedure outlined in

Chapter I may be employed. Thus the following regression

would be performed:

X 1 0 e
S: 1
-e U
X12 1 0 1 e2 (9)
-e U2 U1 e

e 1 1 e
21 21

Equation (9) may also be expressed as follows:

e 12 U1 + e (9')
{ 1 1 21 U2 U1 e t

where I1 and 12 are defined in Chapter I.

However, since Nt is not the same value for all t, this

problem must be solved. In addition, there is potential
heteroskedasticity in the X even without the problem of

the varying Nt values. Both of these problems may be

simultaneously overcome by converting the ordinary least

squares regression in (9) into generalized least squares

form. This procedure involves weighting each of the Xe

and each of the observations of the independent regression

variables. The weights, Wt, are defined below:

W= (10)


S- (11)
xt vTN

and where

Nt e -e 2
E (X. X)2
3t t
S j=l (12)
t N 1 (2)
The multiplication of the Xe and the independent

variables vectors by the Wt values accounts statistically

for the information value of each of the X~. The following

new regression is specified:
-1e 1X
1X 1 0 S-
.xl 1
S-- 1

1 012
-e U
W2X2 12 1 S(13)
W ye 1 1 U U e3
1313 2 1 13
-_S =
x13 x13 x13

W e 1 1
W X S-
21 21 S- S- 21
x21 x21

Another statistical problem is the potential correla-

tion over time of the error terms. Correlation over time

could be indicative that some factor other than option

trading is nonrandomly affecting the equilibrium expected

excess returns on zero systematic risk securities. Among
the factors which could affect r returns in that manner

are changes in expected inflation and changes in the

interest rate term structure. Although these factors are

exogeneous to the capital asset pricing model used in the

theory of Chapter II, movements of these variables to new

equilibrium levels could nevertheless cause trends in the
X time series values. In order to overcome this potential

problem, the regression in (13) above should be repeated

using the Cochrane-Orcutt (1949) procedure of accounting

for correlation over time.

Two Additional Procedures

Two additional problems were discussed earlier, and

must now be considered. The first problem is the possibility

of systematic risk appearing in the 1-month holding period

returns of the neutral hedges. The second difficulty is the

possibility of a change in the risk-return slope during the

time period of the study. This effect could be due to the

new option trading, although this idea has not been

specifically hypothesized here.

As a possible way to overcome the first of these

potential problems a new variable, representative of the
market index, will be introduced into the regression.1 The

CRSP13 Value-weighted index of stock returns, with dividends

reinvested, will be employed as the index variable. Since

new option trading could have a simultaneous effect on the

behavior of the index, an interactive variable is also

added to the regression.

Let Mt denote the value of the market index return for

month t; then define the new interactive variable to be MIt

0 in Period 1, MIt = Mt in Period 2. As before, these 2 new

independent variables must be adjusted for the impact of

heteroskedasticity (differences in the S- ). The full re-
gression model is:

WXe W 0 e
1 1 1 1 0 e

c 2 1
W X W WM M e
1212 12 0 212 0+ 12 (14)

13X3 W13 13 13 13 13 13 13

S2 1

W ye W W WM W M e
21 21 21 21 21 21 21 21 21

Equation (14) may be rewritten in vector form as
equation (14') below:
SI al(14
W j = Wt 12 Mt MIt + et (14')

2 1

Assuming for the moment that Mt measures the true mar-

ket portfolio, the coefficient of Mt in equation (14) is the

systematic risk measure for the hedges for Period 1.

Similarly, the coefficient for MIt in equation (14) is the

change in systematic risk from Period 1 to Period 2. The

coefficient of 12, a2 al in equation (14), would no longer

be interpreted as the change in the mean neutral hedge

return from Period 1 to Period 2. More importantly though,

a2 a" could still be interpreted as the change in the mean

of the zero systematic risk portion of the neutral hedge

returns from Period 1 to Period 2.15 In equation (14) as in

equation (13), the potential serial correlation in the error

terms is to be accounted for by the use of the Cochrane-Orcutt


The second problem concerns the possible impact of a

change in the slope of the (single-period) risk-return re-

lation during the time period of the study--a neutral hedge

return may be affected if the underlying stock is shifting

to a new equilibrium level of expected return. Thus, if

the underlying stocks were, on the whole, imbalanced in terms

of their systematic risk, then the Xt could carry effects

other than changes in the risk-return intercept. Thus, if

the underlying stocks consist of too many high systematic

risk stocks, or too many low systematic risk stocks, and if

the potential risk-return relation shift is significant

enough, then the test outlined above may not capture the

effects of the options on E(r).

The following procedure will be employed to see if

this problem is present: A set of 7 or 8 underlying stocks

with the highest risk is formed; similarly, a set of 7 or 8

underlying stocks with the lowest systematic risk is

assembled. For each of these 2 sets, the neutral hedge

returns are examined by rerunning the basic tests previously

described. If the neutral hedges of the high systematic

risk stocks exhibit similar behavior to the neutral hedges

of the low systematic risk stocks, then the possible shifting

slope of the risk-return relation is most likely not a

significant problem for this study.

The most common systematic risk measure is "beta," which

is proportional to the covariance between a stock's return

and the market return.16 The betas for all of the 30 under-

lying stocks were gathered from Value Line Investment Survey

for the periods included in this study.17 Since Value Line

updates its beta estimates every quarter, the betas on all

of the underlying stocks will generally vary from quarter

to quarter. However, all 8 stocks in Table 4-4a had betas

among the highest 10 of the 30 underlying stocks, each

quarter. Also shown are the betas for each of the 8 stocks

for the beginning, middle and ending quarters of the study.

Similarly, 7 stocks were consistently in the bottom

10 of the underlying stocks, ranked by beta. These 7 stocks

and their betas are shown in Table 4-4b. The empirical

results of the hypothesis tests are presented in Chapter V

along with the naive diversification analysis findings.

TABLE 4-4a
High Beta Stocks

4th 4th 4th
Quarter Quarter Quarter
1973 1974 1975

1. Brunswick 1.73 1.85 1.65
2. Loews 1.78 1.60 1.40
3. McDonalds 1.34 1.55 1.55
4. NW Airlines 1.71 1.70 1.55
5. Pennzoil 1.40 1.40 1.35
6. Poloroid 1.23 1.45 1.40
7. Sperry Rand 1.42 1.30 1.30
8. Texas Instruments 1.32 1.25 1.20

Average "High-Beta" 1.491 1.513 1.425
Average of all 30 stocks 1.178 1.197 1.178

TABLE 4-4b
Low Beta Stocks

4th 4th 4th
Quarter Quarter Quarter
1973 1974 1975

1. ATT .77 .75 .75
2. Merck 1.00 1.00 1.05
3. Exxon 1.12 .85 .95
4. Bethlehem Steel 1.00 1.05 1.05
5. Minn. Mining Manuf. 1.00 1.00 1.05
6. Sears .95 1.00 1.05
7. IBM 1.04 1.05 1.05

Average "Low Beta" .983 .957 .993
Average of all 30 stocks 1.178 1.197 1.178


A popular example of a direct exogeneous influence on
rf is federal monetary policy. Monetary policy which in-
fluences rf could indirectly influence E(r ).

2See Galai (1977).

Black and Scholes claimed this result for neutral
hedges consisting of stock and option positions. However,
the Black-Scholes argument carries over to neutral spreads.
Neutral spreads and hedges are riskless (zero variance)
over the instant of creation and would remain riskless if
investors could continuously revise portfolio positions as
stock prices change. The inability of investors to con-
tinuously rebalance is the source of the variance to neutral
spreads and hedges held for discrete intervals; systematic
risk is presumably not present, however.

The other assumptions from which equation (1) is de-
rived are discussed elsewhere. See, for example, Black and
Scholes (1973), Cox and Ross (1976), Rubinstein (1976b),
Brennan (1979), Cox, Ross, and Rubinstein (1979), and
Rendleman and Bartter (1979).

The conditions are small holding period intervals and
normally-distributed underlying stock prices. See note 3.

Black and Scholes, Galai, and Finnerty also found
neutral hedge returns to be larger than treasury bill yields.
Assuming the mean-variance framework is appropriate, and
assuming the neutral hedges really did not have any systema-
tic risk, the positive excess returns support the Black
equilibrium model over the Sharpe-Lintner model. Thus, the
positive excess neutral hedge returns would, by implication,
also support the contention that, prior to the vast expan-
ion of option trading in the late 1970's, E(rZ) exceeded rf.
To some extent these results are immune to the Roll critique,
since neutral hedge returns are theoretically of zero sys-
tematic risk, Instead of discussing the implications of
their findings in terms of comparing the Black and Sharpe-
Lintner models, the option empiricists considered the
positive excess returns to be evidence of option market
inefficiency. For a different point of view see Phillips
and Smith (1980), who discuss the role of trading costs.

7Boyle and Emanuel show that, if the correlation
between underlying stocks is p, then the correlation between
two neutral hedges is p2

The portfolio variance asymptote found in this study
may be compared to the variance estimates of E(r,) found by
other methods by researchers such as Fama and MacBeth (1974).

It is assumed at this point that "empirical" neutral
hedges, held for one month without rebalancing, actually
have zero systematic risk. The procedure outlined here in
the text suggests a possible way of gauging the amount of
systematic risk in the neutral hedges. Under the procedure
outlined and assuming a large number of different option
hedges are available for portfolio simulation, the portfolio
variance should approach an asymptote of about 1/4 of the
variance of individual hedges. If portfolio variance cannot
be reduced by 3/4, then some systematic risk is probably
in the neutral hedge returns. If the variance, on the other
hand, drops by 3/4, then little or no systematic risk is
probably in the hedges. The reader should be aware that
although each neutral hedge is uncorrelated with the market
portfolio, each neutral hedge does have some correlation
with each other neutral hedge generally. (See note 7.)

1Some reasons why option prices have occasionally been
observed to be below intrinsic value have been pointed out
by Galai (1977, p. 172).

A hedge ratio is the reciprocal of dC/dS. The hedge
ratio is the number of option contracts to short against
100 shares of the stock to create a neutral hedge.

The results of this procedure must be viewed in the
same light as the systematic risk measures obtained by Black
and Scholes, Galai, and Finnerty, because of the implications
of Roll's critique. However, the time series of mean neutral
hedge returns is theoretically uncorrelated with a market
index. Whether (or not) the time series of neutral hedge
returns has significant correlation with respect to the
index may be information that will cause us to have less
(or more) confidence that neutral hedges are really un-
correlated with the "true" market portfolio. See note 9,
also, for the primary way by which the hedges will be
examined for systematic risk.

Center for Research in Security Prices at the Univer-
sity of Chicago.

1This model has been credited to Gujarati (1970).

1This method of checking for systematic risk is
supplementary to the method discussed in note 9.