Unformatted text preview: DeltaHedged Gains and the Negative
Market Volatility Risk Premium
Gurdip Bakshi and Nikunj Kapadia
April 9, 2001 Bakshi is at Department of Finance, Smith School of Business, University of Maryland, College Park, MD 20742, and Kapadia is at Department of Finance, School of Management, University of MassachusettsAmhert, MA 01003. Bakshi can be reached at Tel: 3014052261, Email: [email protected], Website:
www.rhsmith.umd.edu/nance/gbakshi/; and Kapadia at Tel: 4135455643, Email: [email protected]
For helpful comments, we thank Doron Avramov, Charles Cao, Bent Christensen, Sanjiv Das, Stephen Figlewski,
Christopher Jones, Hossein Kazemi, Leonid Kogan, Dilip Madan, George Martin, Vasant Naik, Jun Pan, Jay Patel,
Allen Poteshman, N. R. Prabhala, Rangarajan Sundaram, Bob Whaley, and Gregory Willette. Parts of the article
build on Kapadia's thesis written at New York University. Earlier versions of the paper were presented at Boston
University, University of Massachusetts and Virginia Polytechnic Institute. Conference participants at the 1998
WFA (Monterey) and 2001 AFA (New Orleans) meetings provided many useful suggestions. Nick Bollen (AFA
discussant) and Je Fleming (WFA discussant) provided extremely constructive comments. The reports of Bernard
Dumas (the Editor) and two anonymous referees have substantially improved this paper. Kristaps Licis has provided excellent research assistance. Bent Christensen graciously shared his option dataset. Kapadia acknowledges nancial support from the Center of International Derivatives and Securities Markets. The 1998 version of the paper
was circulated under the title "Do Equity Options Price Volatility Risk?" 0 DeltaHedged Gains and the Negative
Market Volatility Risk Premium Abstract
We investigate whether the volatility risk premium is negative by examining the statistical
properties of deltahedged option portfolios (buy the option and hedge with stock). Within a
stochastic volatility framework, we demonstrate a correspondence between the sign and magnitude of the volatility risk premium and the mean deltahedged portfolio returns. Using a
sample of S&P 500 index options, we provide empirical tests that have the following general
results. First, the deltahedged strategy underperforms zero. Second, the documented underperformance is less for options away from the money. Third, the underperformance is greater at
times of higher volatility. Fourth, the volatility risk premium signicantly aects deltahedged
gains even after accounting for jumpfears. Our evidence is supportive of a negative market
volatility risk premium. 1 The notion that volatility of equity returns is stochastic has a rm footing in nancial economics. However, a less than understood phenomenon is whether volatility risk is compensated,
and whether this compensation is higher or lower than the riskfree rate. Is the risk from changes
in market volatility positively correlated with the economywide pricing kernel process? If so, how
does it aect the equity and the option markets? Evidence that market volatility risk premium
may be nonzero can be motivated by three empirical ndings:
1. Purchased options are hedges against signicant market declines. This is because increased
realized volatility coincides with downward market moves (French, Schwert and Stambaugh
(1987) and Nelson (1991)). One economic interpretation is that buyers of market volatility
are willing to pay a premium for downside protection. The hedging motive is indicative of
a negative volatility risk premium;
2. Atthemoney BlackScholes implied volatilities are systematically and consistently higher
than realized volatilities (Jackwerth and Rubinstein (1996)). A potential explanation for
this puzzling empirical regularity is that the volatility risk premium is negative. Ceteris
paribus, a negative volatility risk premium increases the riskneutral drift of the volatility
process and, thus, raises equity option prices;
3. Equity index options are nonredundant securities (Bakshi, Cao and Chen (2000) and Buraschi
and Jackwerth (2001)). Index option models omitting the economic impact of a market
volatility risk premium may be inconsistent with observed option pricing dynamics.
This article investigates, both theoretically and empirically, whether the volatility risk premium
is negative in index option markets. This is done without imposing any prior structure on the
pricing kernel, and without parameterizing the evolution of the volatility process. The setup is
a portfolio of a long call position, hedged by a short position in the stock, such that the net
investment earns the riskfree interest rate. The central idea underlying our analysis is that if
option prices incorporate a nonzero volatility risk premium, then we can infer its existence from
the returns of an option portfolio that has dynamically hedged all risks except volatility risk.
If volatility is constant, or the price process follows a onedimensional Markov diusion, then
our theoretical analysis implies that the net gain, henceforth \deltahedged gains," on the deltahedged portfolio is precisely zero. A similar conclusion obtains when volatility is stochastic, but
volatility risk is unpriced. In this particular case, we show that the distribution of the deltahedged gains has an expected value of zero. However, if volatility risk is priced, then the sign and
magnitude of average deltahedged gains are determined by the volatility risk premium.
1 Our theoretical characterizations point to empirical implications that can be tested using
discrete deltahedged gains. First, in the timeseries, the atthemoney deltahedged gains must
be related to volatility risk premium. Second, crosssectional variations in deltahedged gains (in
the strike dimension) are restricted by the sensitivity of the option to volatility (i.e., the vega).
Our framework allows us to dierentiate between the volatility risk premium and the jumpfear
underpinnings of deltahedged gains. We test model implications using options written on the S&P
500 index, over the period January 1988 through December 1995. Our empirical specications are
supportive of the following general results: The deltaneutral, positive vega, strategy that buys calls and hedges with the underlying stock signicantly underperforms zero. On average, over all strike and maturity combinations, the strategy loses about 0.05% of the market index, and about 0.13% for atthemoney calls. This underperformance is also economically large; for atthemoney options,
this amounts to 8% of the option value. As predicted by theory, the underperformance is decreasing for options away from the money.
Controlling for moneyness, the underperformance is greater when the hedging horizon is
extended. These results are robust across time, and to the inclusion of put options. At times of higher volatility, this underperformance is even more negative. The losses on
long call positions persist throughout the sample period, and cannot be reconciled by a
downward trending market volatility. Both the crosssectional and timeseries tests provide evidence that support the hypothesis of
a nonzero volatility risk premium. In particular, the results suggest that option prices re
ect
a negative market volatility risk premium. One, the crosssectional regression results indicate
that deltahedged gains are negatively related to vega, after controlling for volatility and option
maturity. Consistent with our predictions, the hedged gains are maximized for atthemoney
options.
1 Buraschi and Jackwerth (2001) and Coval and Shumway (2000) provide additional evidence on the possible
existence of a nonzero volatility risk premium. For instance, the statistical examination in Buraschi and Jackwerth
supports stochastic models with multiple priced factors. Our paper diers from existing treatments in several
respects. First, we provide an analytical characterization that links the distribution of the gains on a deltahedged
option portfolio to the underlying risk sources. Specically, we show a correspondence between the sign of the mean
deltahedged gains and the sign of the volatility risk premium. Economically, the magnitude of the market volatility
risk premium is connected to the value of the option as a hedge. Second, our modeling framework provides an
explicit set of hypotheses for testing whether the market volatility risk premium is negative. Instrumental to this
thrust is whether priced volatility risk or jumps are the primary source of the underperformance of deltahedged
portfolios. For related innovations, we refer the reader to Bakshi, Cao and Chen (1997), Bates (2000), Buraschi
and Jackwerth (2001), Benzoni (1999), Dumas, Fleming and Whaley (1998), Eraker, Johannes and Polson (2000),
Jackwerth and Rubinstein (1996), Jones (2000), Pan (2000), and Poteshman (1998).
1 2 Two, in the timeseries regressions, a higher volatility implies more negative deltahedged gains
for atthemoney options (both using the conventional volatility estimate and a GARCH volatility
estimate). To conrm the hedging rationale underlying a negative volatility risk premium, we
empirically estimate the option vega, and verify that it is strictly positive. Moreover, we show
that options become more expensive (as measured by implied volatility) after extreme market
declines.
Three, we empirically examine whether measures of asymmetry and peakedness of the riskneutral distribution (jumpfear surrogates) are linked to losses on deltahedged strategies. This
investigation nds that, while riskneutral skewness helps explain some portion of the deltahedged
gains for shortdated options, the volatility risk premium retains its explanatory ability even in
the presence of riskneutral skew and kurtosis. Specically, the volatility risk premium is the
predominant explanatory factor for deltahedged gains for short and medium maturities. Our
empirical inquiry also nds that the qualitative patterns of deltahedged gains persist in a precrash sample; the average excess return on the deltahedged option portfolio is negative for both
calls and puts. Finally, we make the observation that deltahedged gains for options bought
immediately after a tail event do not depart considerably between downward and upward daily
return movements. In summary, the jumpfear explanation, although plausible, cannot be the sole
economic justication for the systematic losses incurred on the deltahedged portfolios. However,
stochastic volatility models with a negative volatility risk premium show promise in reconciling
this observation.
The rest of the article is organized as follows. Section 1 formulates our theoretical analysis of
gains on a deltahedged option portfolio. Section 2 discusses the data and variable denitions. The
statistical properties of deltahedged gains are described in Section 3. We conduct, in Section 4, an
empirical exercise that relates deltahedged gains and vega in the crosssection. Section 5 examines
the timeseries implications between deltahedged gains and the volatility risk premium. In Section
6, we analyze whether volatility risk premium explains deltahedged gains in the presence of jump
factors (as re
ected by the riskneutral skew and kurtosis). Finally, concluding remarks are oered
in Section 7. All technical details are in the Appendix. 1 DeltaHedged Gains and the Volatility Risk Premium
This section describes the distribution of the gain on a portfolio of a long position in an option,
hedged by a short position in the underlying stock, such that the net investment earns the riskfree interest rate. We call the gain on the hedged portfolio the \deltahedged gains." We rst
develop the relevant theory in Section 1.1 assuming that volatility of equity returns is constant,
3 and then relax this assumption in Section 1.2 by allowing volatility to be stochastic. The theoretical implications are then used in Section 1.3 to motivate empirical tests about the negative
volatility risk premium. Our analysis also shows how the presence of jumps can contribute to the
underperformance of deltahedged equity portfolios.
To formalize main ideas, let C (t; ; K ) represent the price of a European call maturing in
periods from time t, with strike price K . Denote the corresponding option delta by (t; ; K ).
Dene the deltahedged gains, t;t , as the gain or loss on a deltahedged option position,
where the net (cash) investment earns the riskfree rate:
+ Zt t;t Ct Ct
+ + t + Zt u dSu + t r (Cu u Su ) du; (1) where St is the time t price of the underlying (nondividend paying) stock and r is the constant
riskfree interest rate. In equation (1), we have used the shorthand notation Ct C (t; ) and
t (t; ) = @[email protected] for compactness. The expected t;t can be interpreted as the excess
rate of return on the deltahedged option portfolio. Relating deltahedged gains to the volatility
risk premium is our primary objective throughout.
+ 1.1 DeltaHedged Gains Under Constant Volatility
Let the stock price follow a geometric Brownian motion under the physical probability measure
(with constant drift, , and constant volatility, ): dSt = dt + dW :
t
St (2) 1 By Ito's lemma, we can write the call price as, Ct = Ct +
+ Zt + t u dSu + Zt ! @ Cu + 1 S @ Cu du:
@u 2 u @Su 2 + 2 t 2 2 (3) Standard assumptions also show that the call option price is a solution to the BlackScholes
valuation equation,
1 S @ C + r S @C + @C r C = 0:
(4)
2
@S
@S @t
Using equations (3) and (4), it follows that
2 2 2 2 Ct = Ct +
+ Zt + t u dSu + 4 Zt + t r (Cu u Su) du; (5) which is the statement that the call option can be replicated by trading a stock and a bond.
Combining equation (5) with the denition of deltahedged gains in equation (1), it is apparent
that, with continuous trading, t;t = 0 over every horizon . More generally, it can veried
that t;t = 0 is a property common to all onedimensional Markov Ito price processes: dStt =
S
t[St] dt + t[St] dWt .
When the hedge is rebalanced discretely, t;t will not necessarily be zero. However, Bertsimas, Kogan and Lo (2000) show that the deltahedged gains have an asymptotic distribution that
is symmetric with zero mean. Consider a portfolio of an option that is hedged discretely N times
over the life of the option, where the hedge is rebalanced at each of the dates tn , = 0 1
1
(where we dene t = t, tN = t + ) and kept in place over the period, tn
tn = =N . Dene
the discrete deltahedged gains, t;t , as,
+ + 1 + n 0 ; ; :::N +1 + t;t Ct
+ + Ct N
X 1 n tn (Stn +1 Stn ) =0 N
X 1 n =0
r (Ct tn Stn ) N ; p R (6) C
where tn @Ctn [email protected] . From Bertsimas, Kogan and Lo (2000), N ) p tt Su @@Suu dWu ,
where Wu is a Wiener process, independent of Wu . Thus, the asymptotic distribution of the
discretely hedged option portfolio has a mean of zero, and is symmetric. Simulation results
in Bertsimas, Kogan and Lo as well as those reported in Figlewski (1989) suggests that the
distribution of t;t is centered around zero for a wide range of parameters, and for low values
of N (about 10).
We show next that if we relax the geometric Brownian motion assumption for the stock price
and allow for stochastic volatility outside of the onedimensional Markov diusion context, then
t;t is centered around zero unless volatility risk is priced. Therefore, this setting allows us
construct a test to examine whether the volatility risk premium is negative.
1 2 + 2 2 2 2 1 + + 1.2 DeltaHedged Gains Under Stochastic Volatility
Consider a (twodimensional) price process that allows stock return volatility to be stochastic
(under the physical probability measure), dSt = [S ; ] dt + dW ;
ttt
tt
St
dt = t[t ] dt + t[t] dWt ; (7)
(8) 1 2 where the correlation between the two Weiner processes, Wt and Wt , is . It may be noted
that volatility, t , follows an autonomous stochastic process; the drift coecient, t [t], and the
1 5 2 diusion coecient, t[t ], are functionally independent of St. Therefore, by Ito's lemma, Z t @Cu
Z t @Cu
Zt
Ct = Ct +
bu du;
@S dSu +
@ du +
+ + t + + t u (9) t u C
C
@
where dening bu @Cu + u Su @@Suu + u @@uu + u u Su @SuCu u . The valuation equation
@u
@
that determines the price of the call option is:
1 2 2 2 1 2 2 2 2 2 2 2 1 S @ C + 1 @ C + S @ C + r S @C + ( [ ]) @C + @C r C = 0;
t t @[email protected]
t
t
2 t @S 2 t @
@S
@ @t
2 2 2 2 2 (10) 2 2 2 where t [t] Covt dmtt ; dt represents the price of volatility risk, for a pricing kernel process
m
mt, and Covt(; :) is a conditional covariance operator divided by dt. In general, the volatility risk
premium will be related to risk aversion and to the factors driving the pricing kernel process.
Rearranging (10), it follows that bu is also equal to:
bu = r Cu Su @Cu (u [u] u [u ]) @Cu :
@Su
@u (11) Substituting out bu in the stochastic dierential equation (9), we obtain Z t @Cu
Z t
Z t @Cu
@Cu ) ( [ [) @Cu du:
r(Cu Su @S
Ct = Ct +
u
u
@Su dSu + t @u du + t
@u
t
u
+ + + + (12)
This equation can be further simplied by substituting for du in the second integral to give, Zt
Z t @Cu
@ Cu S du + Z t @Cu du+ Z t @Cu dW : (13)
Ct = Ct +
u @
u @
u
@Su dSu + t r Cu @Su u
t
t
t
u
u
+ + + + + 2 We are now ready to prove the following relationships between deltahedged gains, the volatility
risk premium and the option vega. Proposition 1 Let the stock price process follow the dynamics given in equations (7)(8). Moreover, suppose the volatility risk premium is of the general form t[t]. Then,
1. The deltahedged gains, t;t , is given by
+ Zt t;t =
t
+ + Zt
u[u] @Cu du +
u [u ] @Cu dWu ;
@
@
+ t u and from the martingale property of the Ito integral
Et(t;t ) =
+ Zt + t u @C Et u [u ] u du;
@
u 6 2 (14) (15) where @Ctt represents the vega of the call option, and Et (:) is the expectation operator under
@
the physical probability measure.
2. If volatility risk is not priced in equilibrium, i.e., t[t ] 0, then
Et (t;t ) = O(1=N ); (16) + where the discrete deltahedged gains, t;t , is as dened previously in equation (6).
+ The proposition states that, with continuous trading, if volatility risk is not priced, the deltahedged gains are, on average, zero. In practice, the hedge is rebalanced discretely over time, and
this may bias the average t;t away from zero. However, in (16), we show that this bias is small.
That is, even if we allow for discrete trading, for both the BlackScholes model and the stochastic
volatility model, the mean deltahedged gains is zero, up to terms of O(1=N ). If volatility risk is
priced, equation (15) shows that Et(t;t ) is determined by the price of volatility risk, t, and
the vega of the option, @[email protected]t. The statistical tests in Buraschi and Jackwerth (2001) support
a nonzero volatility risk premium.
There are two specic testable implications that follow from equation (15). First, as the vega is
positive, a negative (positive) t implies that Et (t;t ) will be negative (positive). In particular,
a negative volatility risk premium is consistent with the notion that market volatility often rises
when the market return drops. To see this, consider a LucasRubinstein investor that is long the
market portfolio, and has a coecient of relative risk aversion . Under this particular assumption,
the pricing kernel mt = St . An application of Ito's lemma yields t[t] = Covt dStt ; dt , so
S
that a negative correlation between the stock return and the volatility process implies a negative
t. In our modeling paradigm, there is a onetoone correspondence between the sign of t and
the sign of mean deltahedged gains.
Economically, purchased options are hedges against market declines because increased realized
volatility tends to occur when market falls signicantly. Consequently, in the stochastic volatility
setting, the underperformance of the deltahedged portfolio is tantamount to the existence of a
negative volatility risk premium. Our framework allows one to determine the sign of the volatility
risk premium without imposing any strong restrictions on the pricing kernel process; it also does
not rely on the identication or the estimation of the volatility process. The quantitative strategy
(6) is relatively easy to implement in option markets.
+ 2 + + 2
The distribution of the deltahedged gains can be described in terms of single and multiple Ito integrals. It
is dicult to represent multiple Ito integrals in increments of their component Wiener processes (Milstein (1995)).
p
Therefore, unlike the BlackScholes case, the asymptotic distribution of Nt;t+ cannot be described succinctly. 7 Second, as the option vega is largest for nearthemoney options, the absolute value of Et (t;t )
is also largest for nearthemoney options. If in addition, the volatility risk premium is negative,
as we have hypothesized, the underperformance of the deltahedged portfolio should decrease for
strikes away from atthemoney. We may note that, as option vega's are negligible especially for
deep inthemoney options, these options have little to say about the nature of the volatility risk
premium. Although our focus has been on calls, all the results also apply to puts.
+ 1.3 Testable Predictions
Before we can derive precise empirical implications from equation (15), we need to simplify the
R
righthand term Et tt u @[email protected]u du, in terms of the contemporaneous stock price and the level
of volatility. To streamline discussion, dene g (St; t) t[t ] @Ctt , and consider the ItoTaylor
@
R
expansion of tt g (Su; u ) du (Milstein (1995)):
+ + Zt + t g(Su; u) du = g (St; t) Zt + Z t Z ut
+ + t t 1 Z t Z u L[g(Su0 ; u0 )] du0 du
t
t
Zu
[g (Su0 ; u0 )] dW +
[g (Su0 ; u0 )] dW du;
du + + 1 t 2 2 @
@
@
@
where the (innitesimal) operators are dened as, L[:] = @t [:]+ t St @St [:]+ t @t [:]+ t St @St [:]+
@
@
@
t @t [:] + t Stt @[email protected]@t [:], [:] = tSt @St [:] and [:] = t @t [:]. Using the martingale property
of the Ito integral and equation (15), we, therefore, have
1 2 1 2 2 2 2 2 2 2 2 2 1 2 Z t Z u Et (t;t ) = g (St; t) + Et
L[g(Su0 ; u0 )] du0 du;
t
t
1
X n n @Ct
=
L [t @ ];
t
n (1 + n)!
+ (17) + 1+ (18) =0 by a recursive application of the ItoTaylor expansion. Observe that Et (t;t ) is abstractly
related to the current stock price and volatility, and the parameters of the option price especially
the maturity and moneyness. To develop testable empirical specications, we now exploit certain
option properties and derive the functional form of each term in equation (18). In the discussions
that follow, we assume that all parameters of the option price are held xed.
For a broad class of option models, the call price is homogeneous of degree one in the stock
price and the strike price (Merton (1973)). So, for a xed moneyness, the call price scales with the
price of the underlying asset St . In this case, the option vega, @[email protected]t, also scales with St . We
may therefore separate g (St; t) = t (t; ; y ) St, for option moneyness y , and t (:) independent of
St. If @[email protected]t, and, therefore, g (St; t), scales with St and volatility risk is priced, then we assert
+ 8 that t;t also scales with St , where the scaling factor is a function of t (and other parameters
of the option contract). To prove this, we make use of equation (18), the standard assumption
that the stock price, St , follows a proportional stochastic process and the following property of L
as it operates on a function g :
+ Lemma 1 Consider g(St; t) = t(t; ; y) S , for any 2 R, where t is at most a function
of maturity and volatility. If St obeys a proportional stochastic process, then Ln [g (S; )] is also
proportional to St , for all n 2 f1; 2; 3:::g.
The lemma, which is proved in the Appendix, shows that if g (St; t) scales with St , so does
It follows from equation (18) that t;t is also proportional to St. Thus, we can represent
Et (t;t ) as
Et (t;t ) = St ft [t ; ; y ];
(19) Ln [g]. + + + for some ft [ that is determined by the functional dependence of t and @[email protected]t on t , and
the parameters of the option price, in particular, the option moneyness and maturity. That
Et (t;t )=St varies in the timeseries with physical volatility, t , and in the crosssection (for a xed t ) with the option moneyness, y , forms the basis of the empirical tests.
To derive the crosssectional test, we keep t as xed, and write Et (it;t )=St = ft [ ; yi;t], for
moneyness corresponding to strike price Ki . It is important to keep t as xed, as the option price
is nonlinear in t for away from the money strikes. In the absence of any information regarding
the form of the nonlinearity, it is dicult to specify a model and the corresponding econometric
test that allows both t and yi;t to vary simultaneously. Given a suitable model for ft [ ; y ], we can
then test the relation between Et (it;t )=St and yi;t . Because the vega of the option and thus the
absolute value of Et(it; ) is, ceteris paribus, maximized for atthemoney option, and decreases
for strikes away from atthemoney, it follows that ft [y ] must also be of such a functional form
(controlling for volatility). We can reject the hypothesis of a nonzero volatility risk premium if
we do not nd this hypothesized relation between Et(it;t )=St and yi;t . Thus, the crosssection
of deltahedged gains contains information about the volatility risk premium.
Next, to develop the timeseries relation between E(t;t )=St and t, consider (19) applied
to atthemoney options. It has been noted elsewhere (Stein (1989)) that the shortterm atthemoney call is almost linear in volatility. If Ct is linear in t , @[email protected]t will be independent of t,
and the functional dependence of t;t on t will be determined only by t and the underlying
stochastic volatility process. Given the functional form of t[t ] and the underlying volatility
process, we can infer the functional form of t;t . For atthemoney options, we may specialize
^
ft [t; ; y ] = f t [y ; ] ft[t]. To make this point precise, we develop the functional form of atthemoney t;t for the Heston (1993) model. In his model, the volatility risk premium is linear in
+ + + + + + + + 9 volatility (see also the set of assumptions in Bates (2000), Eraker, Johannes and Polson (2000)
and Pan (2000)). Proposition 2 Consider the special case of the stock price process (7)(8), where [t] = t
and [t] = . Specically, dSt = [S ; ] dt + dW ;
ttt
tt
St
dt = t dt + dWt ; (20) 1 (21) 2 and the volatility risk premium is linear in volatility, as in t[t] = t. Let the call option vega
be proportional to St and independent of t, as in @[email protected]t = t ( ; y ) St. Then, the deltahedged
gains for nearthemoney options must be:
Et (t;t ) = 't( ) St t ; (22) + where 't( ) > 0 is dened in the Appendix. Atthemoney deltahedged gains are negative only if
< 0. Specically for atthemoney options, Proposition 2 shows that if t is proportional to t , so
is the scaled deltahedged gains, Et (t;t )=St. Although not done here, it is straightforward
to extend the analysis to other models, in which case, more generally, Et(t;t )=St may be
a polynomial in t (i.e., Hull and White (1987)). We can, thus, construct a timeseries test
relating the scaled atthemoney deltahedged gains to physical return volatility (or equivalently
the volatility risk premium). We can reject the hypothesis of a zero volatility risk premium if we nd a relation between atthemoney Et (t;t )=St and any functional of physical volatility.
In summary, our theoretical results indicate that the bias in t;t from discrete hedging is
small relative to the impact of a volatility risk premium (as suggested by Proposition 1). Moreover,
the mean atthemoney deltahedged gains (normalized by the stock price) is approximately linear
in the level of physical volatility. We veried both these results via simulations. More exactly,
the deltahedged strategy typically underperforms (overperforms) zero with negative (positive)
volatility risk premium. Additionally, the negative bias is related to the change that occurs
because a negative volatility risk premium increases the option price. In large part, the level of
underperformance is greater with higher volatility. The details are provided in Appendix B.
Before we operationalize and implement the crosssectional and timeseries tests using options
data, one question remains unresolved: How is the performance of deltahedged strategies aected
by jumps? To address this question, we appeal to a jumpdiusion model for the equity price
+ + + + 10 (Bates (2000), Merton (1976) and Pan (2000)). Consider dSt = [S ; ] dt + dW + (ex 1) dq dt;
ttt
tt
t
JJt
St
1 (23) where the volatility dynamics are as displayed in (8). This framework allows for both stochastic
volatility as well as random jumps to aect deltahedged gains. The setup is brie
y as follows.
First, in (23), the variable qt represents a Poisson jump counter with volatilitydependent intensity
J t. Denote the physical density of the jumpsize, x, by q [x]. Second, we posit that x and qt
are orthogonal to each other and to all sources of uncertainty. In addition, if we assume that the
mean of ex 1 is J , the compensator is J J t dt, which is the nal term in (23). Lastly, to
isolate the impact of jumpsize and jump intensity on deltahedged gains, for now, we assume that
only jumpsize is priced. The jump risk premium will therefore introduce a wedge between the
physical density, q [x], and the riskneutral density, q [x]. Specically, assume that the riskneutral
mean of ex 1, is .
J
In the stochastic environment of (23), the deltahedged gains are equal to (see the Appendix): @Cu du + Z t E @Cu S du
Et (t;t ) =
Et u [u ] @
t @S u u
JJ
t
t
u
Z 1
Zt
Z 1u
J
u du
Cu (Su ex ) q[x] dx
Cu (Su ex) q[x] dx : (24) Zt + + + + 1 t 1 The rst term is a consequence of the volatility risk premium, and the other two terms are a
consequence of jumps. When u = 0, equation (24) imparts the intuition that deltahedged gains
are negative provided the mean jump size is negative (i.e., < 0), and there are occasional price
J
discontinuities (i.e., J > 0). In theory, the fatter lefttails of the equity price distribution can lead
to the underperformance of deltahedged portfolios (the sign of return skewness is determined by
the sign of the mean jump size and J controls excess kurtosis). Equation (24) suggests the eect
of jumps on deltahedged gains is most pronounced for inthemoney options. In our extended
framework, the bias in deltahedged gains is partly due to priced volatility risk and partly due to
jump exposures.
Observe that the nal double integral term in (24) is typically negative. This is because
the option price evaluated at the riskneutral density of the jumpsize is generally higher than
under the physical density. Moreover, when the jump risk premium is volatility dependent, as
is the case here, the component of deltahedged gains due to jump risk is related to variations
in volatility. In particular, higher the physical volatility, the more negative are the total deltahedged gains. Now if one additionally assumes that jump intensity is priced (J gets altered
to ), the expression for Et (t;t ) must be modied. Specically, the last two terms must
J
+ 11 R R R u
be replaced by: tt Et @Cu u Su du tt u 1 fCu (Su ex ) Cu (Su )g q [x] dxdu +
J
1
R t R 1 fCJ (S ex) C @S )g q[x] dxdu. JThis analysis suggests that both forms of jump
J t u 1 u u
u (Su
risk will lead to the underperformance of deltahedged portfolios. As we will see, equation (24)
provides the impetus for empirically dierentiating between the negative volatility risk premium
and the jump fear explanations for negative deltahedged portfolio returns.
+ + + 2 Description of Option Data and Variable Denitions
All empirical tests employ daily observations on S&P 500 index options. This equity option
contract is European, and traded on the Chicago Board Options Exchange. The option prices
consists of timestamped calls and puts, and correspond to the last bidask quote reported before
3:00 pm CST. Rubinstein (1994) and Jackwerth and Rubinstein (1996) have suggested that the
precrash and the postcrash index distributions dier considerably. The initial sample date was
accordingly chosen to begin from January 1, 1988 to avoid mixing precrash and postcrash options
(see also Christensen and Prabhala (1998)). Our option sample ends on December 30, 1995.
The option universe is constructed in the following way. First, the option data is screened to
eliminate option prices that violated arbitrage bounds. Specically, we exclude call options whose
price is outside of the range: (Se z e r K; Se z ), for dividend yield z . Second, to minimize
the impact of recording errors, we discard all options that have BlackScholes implied volatilites
exceeding 100%, or less than 1%. Third, we deleted options with maturity less than 14 days.
In addition, all options with maturity longer than 60 days are eliminated. Our present focus
on shortterm options allows us to reduce the impact of stochastic interest rates. Finally, deep
inthemoney option prices can be unreliable due to the lack of trading volume. As in Jackwerth
and Rubinstein (1996) and Buraschi and Jackwerth (2001), deep away from the money options
are omitted. Our option sampling procedure results in 36,237 calls and 35,030 puts.
We require a series for dividends, interest rates and index return volatility. First, following a
prevalent practice, we assume that daily dividends are known over the life of the option contract.
That is, we take the actual dividend payout (from the S&P 500 Bulletin) and subtract the present
discounted value of dividends from the contemporaneous stock price. The adjusted stock price is
employed in our empirical tests throughout.
The interest rates are computed using the procedure outlined in Jackwerth and Rubinstein
(1996) and Buraschi and Jackwerth (2001). Each day, we infer the interest rate using the putcall
parity. Specically, we use a strike price and maturity matched pairs of puts and calls, quoted
within a 1minute interval. The borrowing rate is computed as: rb = (1= ) log (S e z C a + P b )=K .
Likewise, the lending rate is: rl = (1= ) log (S e z C b + P a )=K , for each pair of bidask
12 call and put quotes, (C b ; C a) and (P b ; P a). The daily rate used in the tests is the midpoint of rb
and rl , averaged across all strikes of a specic maturity.
For robustness, we adopt two measures of return volatility. One, we estimate a GARCH(1,1)
model using daily S&P 500 returns over the entire period: Rt = R + t ;
t = a + a t + a t ;
and;
t = t t;
t i.i.d. N (0; 1);
;t 1 2 0 1 2 1 2 2 1 (25)
(26)
(27) where the period return is dened as Rt;t log(St =St) and t is the conditional volatility.
Relying on the GARCH model estimates, the period GARCH volatility estimate is:
+ + v
u
t
g u 252 X ;
VOLt t
^n (28) 2 nt
= where n is the tted value obtained from the GARCH estimation. We experimented with other
^
GARCH specications and obtained similar volatility estimates. The GARCH volatility measure
also allows us to construct a daily volatility series for estimating the hedge ratio in equation (6).
The other volatility measure is the estimate of the sample standard deviation, as in: v
u
t
h = u 252 X Rn
VOLt t
nt ;n 1 R;
2 (29) = where R is now the average daily return. This rolling estimation procedure produces volatility
estimates, with estimation error serially uncorrelated through time for nonoverlapping periods.
To construct an empirical test design that limits overlapping observations, we will sometimes
appeal to a sample of options with constant maturity (for example, 30 days and 44 days). Over
our sample period, the S&P 500 index options have continual option quotes available only for the
two near months. Thus, to build as large a series as possible and yet limit overlap, we employ
options of maturity no more than 60 days.
Dene the option moneyness as y Se r z =K . Consequently, a call (put) option is classied
as outofthemoney if it has moneyness corresponding to y < 1 (y > 1). For reasons already
discussed, our empirical work is restricted to the 10% moneyness range. While outofmoney
puts are sometimes used to sharpen our results, for tractability, much of our analysis centers on
calls.
13 3 Statistical Properties of DeltaHedged Gains
We compute the discrete deltahedged gains for each call option in two steps. hedge ratio, t,
recomputed daily at the close of the day price. The total deltahedged gains for each option up
to the maturity date is then calculated as: t;t = max(St
+ + K; 0) Ct N
X 1 n tn (Stn Stn ) +1 =0 N
X 1 n =0
rn (Ct tn Stn ) N where t = t, tN = t + is the maturity date, and tn is the hedge ratio at tn . In our implementation procedure, the interest rate is updated on a daily basis.
For tractability, tn is computed as the BlackScholes hedge ratio, tn = N [d (Stn ; tn )], where
N [ is the cumulative normal distribution, and
0 1 d 1 p log(yn ) + 1 t;t pn :
2
t;t n
1 + + (30) All our deltahedged calculations allow for timevarying volatility, as re
ected by the use of
GARCH volatility in equation (30). Although the BlackScholes hedge ratio is a reasonable estimate of the true hedge ratio when volatility is not correlated with the stock return process, it will
be biased otherwise. In a later sectionn, we will examine the impact of a misspecied delta.
Panel A of Table 1 provides descriptive statistics for deltahedged gains grouped over maturity
and moneyness combinations. Specically, we report the averages for (i) dollar deltahedged gains
t;t , (ii) deltahedged gains scaled by the index level t;t =St (in %), and (iii) deltahedged
gains scaled by the call price t;t =Ct (in %). For atthemoney calls, and for each maturity, the
deltahedging strategy loses money. On average, over all moneyness and maturities, the strategy
loses about 0.05% of the index level, and for atthemoney calls (i.e., y 2 [ 2:5%; 2:5%]), the
strategy loses about 0.10%. Moreover, the mean t;t =Ct over the full 8 years sample is 12.18%.
It may be noted that the reported standard errors, computed as the sample standard deviation
divided by the squareroot of the number of options, are relatively small. The deltahedged gains
are statistically signicant in all moneyness and maturity categories.
The average loss on the deltahedged strategy of about $0.43 for atthemoney options also
appears high compared with the mean bidask spread of $ . This nding implies that the buyer of
the call (\long" volatility) is paying the seller of the call (\short" volatility) a premium of about
43 cents per call. The economic impact of this premium is substantial, given the large volume of
S&P 500 contracts traded. The S&P 500 trading volume in 1991 was about 11 million contracts,
so that the dollar impact of this premium could be as high as $500 million. The cumulative impact
+ + + + 3 8 14 over the eight year period is of the order of several billion dollars.
We can make two additional empirical observations that appear broadly consistent with a
volatility risk premium. First, the mean deltahedged gains for away from the money strikes are
mostly negative, and less so relative to atthemoney calls. Consider options with moneyness
y 2 [ 7:50%; 5%) versus options with moneyness y 2 [ 2:50%; 0%). In the \All" category,
we can observe that the dollar deltahedged gains is $0.28 versus $0.42. Because the vega
for away from the money options is small, the impact of the volatility risk premium should be
small. Second, the losses on deltahedged portfolios generally deepen when the hedging horizon is
extended from 1430 days to 3160 days. For atthemoney options, the dollar loss over the 3160
days maturity is almost twice than the loss in the 1430 day maturity. This empirical nding tallies
with the theoretical prediction that deltahedged gains should become more negative with maturity
(because the vega is increasing with maturity). Overall, the deltahedged gains are negative except
for deep inthemoney options. That deep inthemoney calls have positive deltahedged gains is
anomalous. We will reconcile this result shortly.
Next, to ensure that the documented results are not driven by extremes, we also examine
the relative outcomes of positive and negative deltahedged gains. The last column displays the
1< statistic that measures the frequency of negative deltahedged gains (consolidated over all
maturities). For atthemoney (outofthe money) options, it is assuring that 68% (76%) of the
observations have negative gains. Therefore, the observation that the mean deltahedged gains
are negative on average, appears robust. Moreover, the frequency of negative deltahedged gains
rise (fall) monotonically when options go progressively outofthemoney (inthemoney). If deep
inthemoney calls are excluded, then as much as 72% of the remaining call sample have negative
deltahedged gains.
As seen from Panel B of Table 1, the results are robust across subsamples (the standard errors
are small and suppressed). In the rows marked SET 1 and SET 2, we report the mean deltahedged gains over the 88:0191:12 and the 92:0195:12 sample periods, respectively. Clearly, the
underperformance of the deltahedged strategy is more pronounced over the second subsample.
In yet another exercise, we examined the sensitivity of our conclusions to any unexpected declines
in index volatility (the deltahedged portfolios suer losses when volatility declines). The deltahedged gains for atthemoney options are negative in 7 out of 8 years. Therefore, the persistent
losses on the deltahedged portfolios cannot be attributable to any secular declines in index volatility. Finally, to verify the results from a dierent options market, we examined deltahedged gains
using options on the S&P 100 index (the details were reported in an earlier version). Reassuringly,
the mean deltahedged gains are also negative for S&P 100 index options. Our conclusions are
robust across sample periods, as well as across both index option contracts.
0 15 Although the conventional estimates of the crosssectional standard errors are small in both
the full sample and the subsamples, these standard errors may not account for the fact that
the theoretical distribution of t;t depends on option moneyness and maturity. We attack this
problem on two fronts. First, we construct representative option timeseries that are homogeneous
with respect to moneyness and maturity. Specically, we take atthemoney call options with a xed maturity of 30 days, 44 days, and 58 days, and deltahedged them until maturity. For 30
days calls, we get a mean = $0:47 with a tstatistic of 2.34. Similarly, the mean for 44 (58)
days options is 0.53 (0.63), with a tstatistic of 2.90 (2.80). Therefore, inferences based on a
homogenous timeseries of deltahedged gains (and standard ttests) also reject the null hypothesis
of zero mean deltahedged gains.
Second, the standard deviation of discrete deltahedged gains in the context of onedimensional
diusions is known from Bertsimas, Kogan and Lo (2000). For BlackScholes, this standardi dehR
=
p
viation equals (see their Theorems 1 and 3): K (1 u ) = exp u = u St =K du .
Even though analytical, the above expression requires estimates of the expected rate of return,
, and the volatility, . We set =11.6% and =11%, to match the average annual index return
and volatility in our sample. For a given strike K, we compute the standard deviation of t;t
at each date t. Standardizing each t;t by the corresponding standard deviation results in a
variable with unit variance. Adhering to a standard practice, we then compute the tstatistic as
the average standardized multiplied by the squareroot of the number of observations. The resulting tstatistics are 5.29, for 30 days options, and 7.30 (9.32) for 44 days and 58 days options.
That the standard deviation of the distribution of t;t decreases with maturity when the hedge
ratio is updated daily is to be expected (see the simulations in Table 1 of Bertsimas, Kogan and
Lo). Reinforcing our earlier results, under BlackScholes, we can easily reject the hypothesis that
Et (t;t ) = 0. We tried other combinations of and , and obtained similar results. It would be
of interest to extend this analysis by theoretically characterizing the distribution of deltahedged
gains under stochastic volatility.
Now return to the result that the deltahedged gains are typically positive for deep inthemoney options, with moneyness greater than 5%. The relative illiquidity for inthemoney calls
may upwardly bias the mean deltahedged gains. Because there is not much trading activity, the
market makers often chooses not to update inthemoney call prices in response to small changes in
the index level (Bakshi, Cao and Chen (2000)). We believe that illiquidity of inthemoney options
may lead to positive deltahedged portfolio returns. To verify this conjecture, and to understand
the sources of this phenomena, we examine, in Table 2, deltahedged gains for outofmoney puts,
which are equivalent to inthemoney calls. Relative to inthemoney calls, the outofmoney puts
are more actively traded. Supportive of our conjecture, and in contrast to the empirical results
+ 1 2 0 2 12 2 2+log 12 2 1+ + + + + 16 from inthemoney calls, the outofmoney put deltahedged gains are now strongly negative: The average deltahedged gains are $1.03 and $0.82 for put options with moneyness y 2
[5%; 7:5%) and y 2 [7:5%; 10:0%), respectively. It is evident that the losses on the deltahedged put portfolios is robust to samples restricted by strikes, maturity and time periods; When deep inthemoney calls (beyond 5%) are combined with deep outofthemoney puts, the mean dollar deltahedged gains are $0.14, and of absolute magnitude less than that for
all atthemoney calls and puts. To sum up, when we combine the results from calls and puts, for the vast majority of the options
that are actively traded, the deltahedged gains are overwhelmingly negative. This evidence on
the underperformance of deltahedged portfolios, among calls and puts, is strongly supportive
of a negative volatility risk premium. In a spirit similar to ours, Coval and Shumway (2000)
corroborate that (long volatility) atthemoney S&P 500 straddles produce average losses of about
3% per week. That the sign of the market volatility risk premium is negative is in agreement
with the parametric approach adopted in Eraker, Johannes and Polson (2000) and Pan (2000)
(i.e., the negative volatility risk premium increases the riskneutral drift of the volatility process).
Exploiting the spanning properties of options, the results in Buraschi and Jackwerth (2001) suggest
the possibility of a nonzero volatility risk premium. Although stochastic volatility option models
have been shown to reduce tting errors (Bakshi, Cao and Chen (1997)), a negative volatility risk
premium oers the further potential for reconciling option prices.
Under the premise that higher volatility implies a more negative volatility risk premium, is
it empirically true that higher volatility translates into greater underperformance of deltahedged
portfolios? To investigate this issue, we constructed the two measures of volatility outlined in
(28) and (29), and binned the atthemoney deltahedged gains into 7 volatility groups (< 8%,
810%, 1012%, 1214%, 1416%, 1618%, and >18%). To save on space, we maintain focus on the
conventional measure of volatility displayed in (29). The pattern of average deltahedged gains
in Table 3 validates several results of economic relevance. First, consistent with a volatility risk
premium, the returns on deltahedged portfolios are inversely proportional to volatility: during
times of higher return volatility, the deltahedged gains get even more negative. This is true
irrespective of whether the underperformance is measured in dollar terms or as a fraction of the
index level. Second, the ndings are invariant to outliers. In most volatility groups, the median
deltahedged gains is more negative than the mean deltahedged gains, and generally decline with
increase in volatility. Volatility is an important source of the underperformance of deltahedged
portfolios.
17 4 DeltaHedged Gains and Option Vega in the CrossSection
We consider next the crosssectional implication of the volatility risk premium. Following Section
1.2 (equation (19)), for a xed t , t;t =St must be related to the option vega, such that mean
deltahedged gains decrease in absolute magnitudes for strikes away from atthemoney. We test
this implication by adopting the econometric specication,
+ GAINSit = + VEGAit + eit ;
0 i = 1; ; I; 1 (31) where GAINSit t;t =St and VEGAit is the option vega (indexed by moneyness i = 1; ; I ).
While controlling for volatility and option maturity, equation (31) models the proportionality
of deltahedged gains in the option vega. The null hypothesis that volatility risk is not priced
corresponds to = 0.
For estimating equation (31), we require a proxy for VEGAit , and a procedure for controlling
for volatility. To demonstrate robustness of the crosssectional regression estimates, the option
vega is approximated in two dierent ways:
+ 1 8
< exp d =2
VEGA = :
jy 1j BlackScholes Vega,
Absolute Moneyness, 2
1 (32) where d is as presented in equation (30). Two points are worth emphasizing about (31)(32).
First, because exp( d =2) reaches a maximum when the strike is atthemoney, a negative (positive) volatility risk premium corresponds to < 0 ( > 0). Furthermore, the magnitude of + is approximately the mean deltahedged gains for atthemoney options. Note that the
average volatility embedded in d serves simply as a scaling factor for log(y ) and governs the rate
of change in exp( d =2), as the option moneyness moves away from the money. For example, for
a 30 days option evaluated at 12% volatility, the impact of the risk premium on a 4% away from
the money option is half that for atthemoney options. This rate of decrease is slower for higher
levels of volatility.
Second, the function jy 1j reaches a minimum for atthemoney options. In this case, the
hypothesis of a negative (positive) volatility risk premium corresponds to < 0 and > 0
( > 0 and < 0). In this model, the mean deltahedged gains for atthemoney options is
precisely . Both approximations, exp d =2 and jy 1j, plausibly characterize the behavior
of the option vega.
It is necessary that the sample for each estimation of equation (31) consists of a panel of deltahedged gains where the historically measured volatility is approximately constant. To achieve
1 2
1 1 0 1 1 1 2
1 0 0 1 0 2
1 18 1 this, we divide the sample period into intervals of 2%; within each sample, we include all dates
where the volatility is within one of these intervals. Therefore, we assume the constancy of the
volatility risk premium within a volatility classication. To increase the power of the test, and
because the sensitivity of the vega (and, thus, the deltahedged gains) to moneyness is more
pronounced at shorter maturities, we estimate equation (31) for 30 and 44 days options. With
two vega surrogates, we thus have 28 distinct panels, with volatility approximately ranging from
6% to 20%, and with panel size ranging from 46 to 283 observations.
When implementing (31), one econometric issue arises. As there are multiple observations of
option prices on each date within a volatility sample, it is possible that there is a datespecic
component in t;t that needs to be explicitly modeled. We follow standard econometric theory
(see, for example, Greene (1997)) and allow for either a datespecic xed eect, or a datespecic
random eect. In the xed eects model, we replace in equation (31) by ;t . In the random
eects model, we allow for a component of the disturbance to be datespecic, as modeled by
eit = ut + vti. We conduct specication tests on our samples, and, in the majority of the samples,
the Hausman test of xed versus random eects and a Lagrange Multiplier test of random eects
versus OLS favors the random eects specication. As a consequence, all reported results are
based on the random eects model, where the coecients are estimated by Feasible Generalized
Least Squares panel regression (hereby FGLS).
Table 4 supports the central implication that the volatility risk premium is negative. Consider rst 30 days options and vega measured by exp( d =2). In this case, as hypothesized, the coefcient is persistently negative. The regression coecient ranges between 0.67 and 0.06,
and implies a negative volatility risk premium. For 5 out of 7 volatility levels, the coecient is
statistically signicant with a minimum (absolute) zstatistic of 2.95 (shown in square brackets).
The estimate of + are roughly in line with the ndings in Table 1 and Table 3: the mean
deltahedged gains are more negative for higher volatility regime versus lower volatility regimes.
For instance, the estimate of + is 0.13% in the 810% volatility grouping in comparison to
0.41% in the 1416% volatility grouping. Based on the Wald test, the hypothesis + = 0
is rejected at the usual signicance level (for most groups). Since the R is not particularly instructive for panel regressions, it has been excluded. The results for the 44 days options are
comparable with 5 out 7 signicantly negative coecient. Therefore, for both maturities, the
absolute value of deltahedged gains are maximized for atthemoney options, and decrease with
the option vega.
When vega is proxied by jy 1j, there is evidence for the joint hypothesis that < 0 and > 0. For 30 days options, varies from a low of 1.02 to a high of 6.78, and statistically
signicant in 5 out of 7 estimations. The estimated coecient and the associated tstatistics
+ 0 0 2
1 1 1 0 1 0 1 0 1 2 1 0 1 1 0 19 allow us to reject the hypothesis that Et(t;t =St) is zero (in 5 out of 7 volatility groups). As
before, the results from 44 days options are consistent with those from 30 days options. Both sets of
estimations verify that mean deltahedged gains decrease in absolute magnitudes for strikes away
from atthemoney. Our evidence supports the crosssectional implication of a negative market
volatility risk premium.
In Table 5, we provide additional conrmatory evidence for 30 days options. First, in Panel A,
we report the results from a panel regression when (31) is altered to: GAINSit = + VOLh
t
i + ei (i = 1; ; I ). In this specication test, we also allow the mean deltahedged gains to
VEGAt t
vary with volatility. For example, the time t atthemoney deltahedged gains are now represented
by + VOLt. As observed, the results reported in Panel A of Table 5 and those reported
in Table 4 are mutually consistent. Second, Panel B of Table 5 substantiates that similar results
can be found in the subsamples. Therefore, our key ndings are robust across subsamples and to
modications in the test specications.
To summarize, the crosssectional regressions support three main empirical results. The rst
conclusion that emerges is that we can formally reject the hypothesis that Et [t;t =St] = 0.
Moreover, the signs of the estimated coecients are compatible with a negative volatility risk
premium. Finally, the deltahedged gains are maximized for atthemoney options, and decrease
in absolute value for moneyness levels away from atthemoney. Each nding is consistent with
the theoretical predictions.
A negative market volatility risk premium has the interpretation that investors are willing to
pay a premium to hold options in their portfolio, or that a long position in an index option acts
as a hedge to a long position in the market portfolio. We illustrate this point from two dierent
angles. First, we directly examine how option prices react to volatility. For a xed option maturity,
we build a monthly timeseries of atthemoney call option prices (divided by the index level) and
regress it on historical volatility (as estimated in equation (29) for = 30 days):
+ 0 0 1 1 + 30 Days: Ct=St = 0.004 +0.05VOLh +0.44 Ct =St
t
1 1 1 1 + et , R =43.16%, DW=2.01,
2 [3.36] [3.23]
[4.12]
44 Days: Ct=St = 0.003 +0.06VOLh +0.52 Ct =St + et, R =65.23%, DW=2.28.
t
[2.26] [3.89]
[5.18]
Controlling for movements in the index level through time, these regressions show, as would be
expected, that call prices respond positively to volatility. To put the estimated slope coecient
in perspective, we note that, in our sample, the average C/S is 1.70%, and the average volatility
is 11%. An increase in the level of volatility from 11% to 12% will increase C/S from 1.70% to
20 2 1.79%. This increase is the order of magnitude as that implied by the 30 days atthemoney BlackScholes vega. Given the extensive evidence on the negative correlation between stock returns and
volatility, the positive estimate of the empirical vega conrms the hedging role of options.
To highlight the value of the option as a hedge during signicant market declines, we contrast
the change in the relative value of index options for the largest 20 negative and positive daily
returns (roughly a 3 standard deviation event). On the day prior to a tail event, we buy a nearesttothemoney shortterm call option, and compute the BlackScholes implied volatility. Proceeding
to the day after the tail event, we recompute the BlackScholes implied volatility for the prevailing
nearesttothemoney calls. For each of the largest extreme movement, Table 6 reports (i) the
(annualized) implied volatilities, and (ii) the corresponding change in implied volatility as a fraction
of the implied volatility of the option bought. We can observe that the average change (relative
change) in the implied volatility is 1.71% (10.58%) to a downward movement versus 0.84% (1.53%) to an upward movement. Holding everything else constant, the index options become more
expensive during stock market declines (in 18 out of 20 moves, the implied volatility increases).
On the other hand, when the market has a strong positive return, the eect on option values is not
as striking. These ndings further support our assertion that equity index options are desirable
hedging instruments. 5 DeltaHedged Gains and the Volatility Risk Premium: TimeSeries Evidence
Following Proposition 2, we now consider the timeseries implications of the volatility risk premium
for atthemoney options. Fixing option maturity, we estimate the timeseries regression:
GAINSt = + VOLt + GAINSt + t ;
0 1 2 1 (33) where GAINSt represents the dollar deltahedged gains for atthemoney options divided by the
index level, and VOLt is the estimate of historical volatility computed over the 30 calendar day
period prior to t (see equation (28) for VOLg , and equation (29) for VOLh ). In the timeseries
t
t
setting of equation (33), testing whether volatility risk is not priced is equivalent to testing the
null hypothesis = 0. Observe that we have added a lagged value of GAINS to correct for the
serial correlation of the residuals. The estimation is done using OLS, and the reported tstatistics
are based on the NeweyWest procedure (with a lag length of 12). As a check, we also estimate
the model using the CochraneOrcutt procedure for rstorder autocorrelation. Since the results
are virtually the same, they are omitted to avoid duplication.
1 21 To ensure that the regression results are not an artifact of option maturity, we perform regressions at the monthly frequency using deltahedged gains realized over (i) 30 days, (ii) 44 days
and (iii) 58 days. Although the 30 days series for deltahedged gains is nonoverlapping, a partial
overlap exists with 44 days and 58 days series. To begin, consider the 30 days series for VOLh .
t
The results of Table 7 show that the OLS estimates of the volatility coecient, , are negative
and statistically signicant in all the samples. Over the full sample, the estimated is 0.032
with a tstatistic of 4.39. In addition, the serial correlation coecient, , is negative with a
tstatistic of 3.47. The inclusion of GAINSt leads to residuals that show little autocorrelation,
as is evident from the BoxPierce statistic with 6 lags (denoted as Q ). The coecient is
comparable across maturities, and is signicantly negative throughout.
The empirical t of the regressions is reasonable, with the adjusted R higher for each of the
two subsamples. Furthermore, the magnitude of the coecient is an order smaller than that of . Overall, our results seem to indicate that variations in atthemoney deltahedged are related
to variations in historical volatility. This result also holds when volatility is measured by VOLg .
t
h . This indicates that a measure of
However, the adjusted R 's are consistently higher with VOLt
volatility that puts more weight on the recent return history has greater explanatory power, and
is more informative about deltahedged portfolio returns. Our repeated nding that < 0 has
the implication that the market volatility risk premium is negative.
Is the magnitude of the risk premium indicated by economically signicant? Consider again
the 30 days series for VOLh . Evaluating (33) at the estimated parameter values, we estimate the
t
eect of the volatility risk premium as measured by the implied dollar deltahedged gains (at three
representative volatility levels):
1 1 2 1 6 1 2 0 1 2 1 1 1. On August 19, 1992, the volatility level was 8.05% with atthemoney call price and index
level of $5.44 and 418.67, respectively. The volatility risk premium is 3.63% of the call
option value;
2. Now consider July 19, 1989, where the volatility level was 12.04% with atthemoney call
price and index level of $6.19 and 334.92, respectively. The volatility risk premium is 11.18%
of the call option value;
3. Finally, on November 20, 1991, the volatility level was 15.86% with atthemoney call price
and index level of $6.94 and 378.80, respectively. In this case, the volatility risk premium is
19.60% of the value of the call.
Overall, the magnitudes of the volatility risk premium embedded in atthemoney deltahedged
gains are plausible and economically large. The impact of the volatility risk premium is more
22 prominent during times of greater stock market uncertainty. As emphasized in the previous
section, this eect may be related to demand for options as hedging instruments. 5.1 Robustness of Findings
Several diagnostic tests are performed to examine the stability of . First, we reestimated the
regression using the variance as an explanatory variable, with no material change in the results.
This last conclusion is not surprising as the standard deviation and variance are highly correlated.
In fact, a model with both variables included performs worse than a model with either of these
variables. This suggests that not much can be gained by modeling t;t =St as a polynomial in
volatility.
Second, to evaluate whether the results are sensitive to a trending stock market, we reestimated the model using dollar deltahedged gains, t;t . Again, the results were invariant
to this change in specication. Third, we explored the possibility that volatility may be nonstationary. To investigate the impact of nonstationarity on the parameter estimates, we performed an OLS estimation in rst dierences rather than in levels. This extended specication
again points to a negative (these results are available upon request). The principal nding that
the market volatility risk premium is negative is robust under alternative specications.
A natural question that arises is: How sensitive are the results to the mismeasurement of the
hedge ratio? Extant theoretical work suggests that the BlackScholes hedge ratio can depart from
the stochastic volatility counterpart when volatility and stock returns are correlated. Guided by
this presumption, we now examine (i) whether a negative correlation biases the estimate of t;t ,
and, if so, (ii) whether our conclusions about the negative volatility risk premium are robust. For
each maturity, we assemble a timeseries of atthemoney calls where the return, Rt;t , is positive.
For this sample, it is likely that underhedging (overhedging) results in higher (lower) deltahedged
gains.
We estimate the regression: GAINSt = + VOLh + GAINSt + Rt;t + t , with the
t
additional variable added to capture the eect of systematic mishedging. In a trending market, we
expect > 0, if the call is consistently underhedged, and < 0, if it is overhedged. Although not
reported in a table, two ndings are worth documenting. First, even when we explicitly account
1 + + 1 + + 3 0 1 3 2 1 3 + 3 The logic behind this exercise can be explained as follows. Suppose that the dierence between the true
hedge ratio and the BlackScholes hedge ratio is t (t ; y), where > 0 if BlackScholes underhedges and negative
otherwise. t+ the denition of deltahedged gains, it immediately follows that the bias in its estimate is equal to:
R From
R
R
t;t+ = t u dSu tt+ ru Su du, which has an expected value of tt+ ( r)Su du, where is the drift of the
price process and represents the expectation of (assuming is independent of the entire path of Su ). Thus, the
expected deltahedged gains is of the order of the market risk premium. If r > 0, and BlackScholes underhedges
the call, then the estimated deltahedged gains is biased upwards.
3 23 for the impact of under or overhedging, the coecient , is signicantly negative. Second,
the coecient, , is positive, and hence t;t is upwardly biased. However, in none of the
regressions is statistically signicant. That is signicantly negative appears robust to errors
in hedging arising from a correlation between the stock return process and the volatility process.
One interpretation is that the hedge ratio takes into consideration timevarying GARCH volatility
and is therefore less misspecied. That a misspecied hedge ratio cannot account for the large
negative deltahedged gains that are observed for atthemoney options is also the conclusion of
our simulation results below.
One nal cause of concern is that the theoretical distribution of deltahedged gains may vary
across the sample set in a complex manner. Therefore, standard procedures adopted in estimating
(33) may not fully account for changes in the covariance matrix of t;t =St . To explore this, we
repeated our estimation using generalized method of moments (Hansen (1982)). The instrumental
variables are a constant and three lags of volatility. For options of maturity 30 days, the estimated is 0.045 with a tstatistic of 5.08 (using NeweyWest with 12 lags). The minimized value of the
GMM criterion function, which is distributed (2), has a value of 2.77 and a pvalue of 0.24. The
results are similar for options of 44 and 58 days. Thus, we do not reject the empirical specication
in equation (33). The volatility risk premium coecient, , is signicantly negative in line with
our earlier ndings.
1 3 + 3 1 + 1 2 1 5.2 Simulation Evidence
Since the empirical tests reject the null hypothesis that volatility risk is unpriced, we pose two
additional questions using simulated data: (i) How severe is the small sample bias?, and (ii) What
is the impact of using BlackScholes hedge ratio as the approximation for the true hedge ratio? For
this articial economy exercise, our null hypothesis is that volatility is stochastic, but not priced.
Therefore, we set [t] = 0, so that the dynamics of t requires no measure change conversions.
We simulate the paths of f(St; t) : t = 1; ; T g, according to (50)(51). To be consistent with
our empirical work, the simulated sample path is taken to be 8 years (2880 days).
At the beginning of the month, an atthemoney call option is bought and deltahedged discretely over its lifetime. Proceeding to the next month, we repeat this deltahedging procedure.
The option price is given by the stochastic volatility model of Heston (1993). For comparison, the
deltahedged gains are computed using the hedge ratio from the true stochastic volatility option
model as well as using the BlackScholes model. Across each simulation run, we generate 96 observations on deltahedged gains and the prior 30 days volatility. Using the simulated sample, we
estimate (33). In Table 8, we report the sample distribution of estimated coecients over 1000
24 simulations for two option maturities, 30 days and 44 days (the mean, and the mean absolute
deviation in curly brackets). The rst point to note is that with unpriced volatility risk, the mean
deltahedged gains are virtually zero. Under the stochastic volatility model, the magnitude of =S
is of several orders lower than those depicted in Table 1. Second, the use of BlackScholes delta
imparts a negligible bias. For example, for 30 days options, the mean =S is 0.0018% with the
stochastic volatility hedge ratio versus 0.0022% with BlackScholes hedge ratio. In conclusion, the
simulations show that the use of BlackScholes hedge ratio does not perversely bias the magnitude
of deltahedged gains.
Now shift attention to the sample rejection level of the estimated coecients from the simulated
data. First, given the theoretical pvalue benchmark of 5%, the null hypothesis = 0 should
be rejected only occasionally. Again consider stochastic volatility model with option maturity of
30 days. Inspection of Table 8 shows that the frequency of t( ) < 2 is 3.94%. Moreover, the
frequency of t( ) > 2 is 1.21%. Therefore, when combined, there is only a small overrejection
of the null hypothesis. If the hedge ratio is replaced with the BS delta, the simulated rejection
frequency is again close to the theoretical 5%.
Because the 44day options allow for some overlap in the data, we expect to see autocorrelation
and, therefore, worse small sample properties. The simulations conrm that the frequency of the
rejection of the hypothesis of = 0 is slightly higher at 7.1%. However, the overlap does not aect
the estimate of the mean =S (which is 0.0017%); neither does it worsen the t with the BlackScholes hedge ratio. Overall, the simulation evidence suggests that small sample biases are not
large, and that the use of the BlackScholes hedge ratio has negligible eect on the estimations.
Having said this, we can now proceed to examine the jumpfear foundations of negative deltahedged portfolio returns.
1 1 1 1 6 DeltaHedged Gains and Jump Exposures
While the body of evidence presented so far appears consistent with a volatility risk premium, the
losses on the deltahedged portfolios may also be reconciled by the fear of stock market crashes.
The underlying motivation is that option prices not only re
ect the physical volatility process
and the volatility risk premium, but also the potential for unforeseen tail events (Jackwerth and
Rubinstein (1996)). Jump fears can therefore dichotomize the riskneutral index distribution from
the physical index distribution, even in the absence of a volatility risk premium. Indeed, empirical
evidence indicates that the riskneutral index distribution is (i) more volatile, (ii) more leftskewed,
and (iii) more leptokurtotic, relative to the physical index distribution (Bakshi, Kapadia and
Madan (1999), Jackwerth (2000) and Rubinstein (1994)). As our characterization of deltahedged
25 gains shows in (24), these distributional features can induce underperformance of the deltahedged
option strategies. If, in addition, the jump risk premium surfaces more prominently during volatile
markets (Bates (2000), Eraker, Johannes and Polson (2000) and Pan (2000)), then it can account
for the accompanying greater deltahedged losses.
To empirically distinguish between the eects of stochastic volatility and jumps on deltahedged gains, two decisions are made at the outset. One, in the tradition of Bakshi, Kapadia and
Madan (1999), Bates (2000) and Jackwerth and Rubinstein (1996), we assume that jump fears
can be surrogated through the skewness and kurtosis of the riskneutral index distribution. In
the modeling framework of (23), the mean jumpsize governs the riskneutral skew, and the jump
intensity is linked to kurtosis. For instance, the fear of market crashes can impart a leftskew,
and shift more probability mass towards low probability events. Two, the riskneutral skews and
kurtosis are recovered using the modelfree approach of Bakshi, Kapadia and Madan (1999). They
show that the higherorder riskneutral moments can be spanned and priced using a positioning
in outofmoney calls and puts. In what follows, the relative impact of jump fears on deltahedged
gains is gauged from three perspectives.
First, we modify the timeseries specication (33) to include a role for riskneutral skew and
kurtosis, as shown below:
GAINSt = + VOLh + GAINSt + SKEW + KURT + ;
t
t
t
t
0 1 1 2 3 4 (34) where VOLh is the historical volatility, SKEW is the riskneutral index skewness and KURT is
t
t
t
the riskneutral index kurtosis. For convenience, the exact expressions for skew and kurtosis are
displayed in (45) and (46) of the Appendix. Specically, the riskneutral skewness and kurtosis
re
ect the price of the cubic contract and the kurtic contracts, respectively. As before, we include a
lagged value of deltahedged gains to correct for serially correlated residuals. The estimation is by
OLS, and the tstatistics are computed using the NeweyWest procedure with 12 lags. The main
idea behind the empirical specication (34) is to investigate whether physical volatility loses its
signicance in the presence of such jump fear proxies as riskneutral skews and kurtosis. We also
employed the slope of the volatility smile and the Bates skewness premium measure as alternative
proxies for jump fear, and obtained similar conclusions (details are available from the authors).
To maintain the scope of the investigation, these extended measures are excluded from the main
body of the paper.
Before we discuss the estimation results presented in Table 9, it must be stressed that there
is substantial evidence of jump fear in the postcrash riskneutral distributions. Over the entire
sample period, the average riskneutral skewness is 1.38 and the riskneutral kurtosis is 7.86, for
26 30 day distributions. These numbers are roughly comparable to those reported in Jackwerth and
Rubinstein (1996) for longerterm options, and in Bakshi, Kapadia and Madan (1999) for the S&P
100 index options. The most important point that emerges from Table 9 is that historical volatility
continues to signicantly aect variations in deltahedged gains. The coecient on volatility
ranges between 0.111 to 0.041, and is statistically signicant in all the nine estimations. The
evidence on the role of skew and kurtosis is less conclusive. Although skew enters the regression
with the correct sign, it is only marginally signicant. The positive estimate of indicates
that a more negatively skewed riskneutral distribution makes deltahedged gains more negative
from one month to the next. In addition, the sign of kurtosis is contrary to what one might
expect. While not reported, skewness (kurtosis) is not individually signicant when volatility and
kurtosis (skewness) are omitted as explanatory variables in (34). Finally, comparing the empirical t between Table 7 and Table 9, the inclusion of skew and kurtosis only modestly improves the
adjustedR (by about 3%). In summary, this exercise suggests that volatility may be of rstorder
importance in explaining negative deltahedged gains.
In the second exercise, we study the behavior of deltahedged option portfolios for a holdout
sample when jumpfears are much less pronounced. For this purpose, we selected the sixmonth
interval from January 1987 through June 1987 (option data provided by Bent Christensen). What
is unique about this precrash period is that riskneutral index distributions are essentially lognormal. Especially suited for the task at hand, the jumpfears are virtually lacking during this
precrash sample (Jackwerth and Rubinstein (1996)). Table 10 reports the mean deltahedged
gains for outofmoney calls and puts. The average deltahedged gains for nearthemoney 1430
days calls (puts), is $0.65 ($0.82). In fact, the deltahedged gains are strongly negative in all the
16 moneyness and maturity categories. Furthermore, the majority of the options have < 0, as
seen by the large 1< statistics. The deltahedged gains are negative in both the precrash and the
postcrash periods. While not displayed, the average implied volatility for atthemoney options
is higher than the historically realized volatility suggesting that the wellknown bias between the
implied and the realized volatility predates crashfears and option skews.
In the nal evaluation exercise, we compute the average deltahedged gains for the largest
downward and upward market movements. Intuitively, if fears of negative jumps are the predominant driving factor in determining negative deltahedged gains, then there should be a strong
asymmetric eect on the deltahedged gains (see (24)), with large positive index returns not
necessarily resulting in large negative deltahedged gains. In contrast, it may be argued that a
negative volatility risk premium would cause large negative deltahedged gains, irrespective of the
sign of the market return. To brie
y examine this reasoning, consider closest to atthemoney
shortterm calls bought on the day subsequent to a tail event. Respectively for the largest 10
3 2 0 27 (20) tail events, the average scaled deltahedged gains,t;t =St, are 0.52% (0.43%) on positive
returns dates, compared with 0.86% (0.51%) on negative return dates. The evidence indicates
that deltahedged gains become more negative for both extreme negative and positive returns. In
eect, this evidence from the extremes is consistent with the regression results.
One overall conclusion that can be drawn is that priced volatility risk is a more plausible
characterization for negative deltahedged gains. While it is possible that if some extremely
low probability event is included, the resulting large positive gain may wipe out all cumulative
losses. However, this low probability event (of the required magnitude) has yet not occurred in
our sample. Our key nding that the market volatility risk premium is, on average, negative is
mutually consistent with other evidence reported in Benzoni (1999), Jones (2000), Pan (2000) and
Poteshman (1998).
+ 7 Final Remarks
Is volatility risk premium negative in equity index option markets? We argue that the central
implication of a nonzero volatility risk premium is that the gains on a deltaneutral strategy
that buys calls and hedges with the underlying stock are nonzero, and determined jointly by
the volatility risk premium and the option vega. Specically, we establish that the volatility risk
premium and the mean discrete deltahedged gains share the same sign. It is shown that this
implication can be tested by relatively robust econometric specications in either the crosssection
of option strikes, or in the timeseries. These tests do not require the identication of the pricing
kernel, nor the correct specication of the volatility process.
Using S&P 500 index options, our empirical results indicate that the deltahedged gains are
nonzero, and consistent with a nonzero volatility risk premium. The main ndings of our investigation are summarized below:
1. The deltahedged call portfolios statistically underperform zero (across most moneyness and
maturity classications). The losses are generally most pronounced for atthemoney options.
2. The underperformance is economically signicant and robust. When outofmoney put options are deltahedged, a similar pattern is documented. The documented underperformance
of deltahedged option portfolios is consistent with a negative volatility risk premium.
3. Controlling for volatility, the crosssectional regression specications provide support for the
prediction that the absolute value of deltahedged gains are maximized for atthemoney
options, and decrease for outofthemoney and inthemoney options.
28 4. During periods of higher volatility, the underperformance of the deltahedged portfolios worsens. As suggested by the hypothesis of a negative volatility risk premium, timevariation in
deltahedged gains of atthemoney options are negatively correlated with historical volatility. This nding is robust across subsamples, and to mismeasurement of the hedge ratio.
5. Finally, volatility signicantly aect deltahedged gains even after accounting for jumpfears.
Jump risk cannot fully explain the losses on the deltahedged option portfolios.
In economic terms, a negative volatility risk premium suggests an equilibrium where equity
index options act as a hedge to the market portfolio, and is consistent with prevailing evidence
that equity prices react negatively to positive volatility shocks. Thus, investors would be willing
to pay a premium to hold options in their portfolio, and this would make the option price higher
than its price when volatility is not priced. The empirical results of this paper indeed strengthen
the view that equity index options hedge downside risks.
There are two natural extensions to this paper. First, given that volatilities of individual stocks
and the market index comove highly, one could examine whether the volatility risk premium is
negative in individual equity options. The crosssectional restrictions on deltahedged gains and
the volatility risk premium can be tested in the crosssection of individual equity options. Second,
volatility risk is of importance in almost every market. The analysis conducted here can be directly
applied to include other markets such as foreign exchange and commodities. Much more remains
to be learned about how volatility risk is priced in nancial markets. 29 Appendix A: Proof of Results
Proof of Proposition 1: We need to show that Et(t;t ) = O(1=N ), when t[t] 0. First,
without loss of generality, assume r = 0, t @[email protected] and vt t @[email protected]t. Second, let the
+ period corresponding to the time to expiration, t = 0 to t = t + , be divided equally into N
periods, corresponding to dates, tn , n = 0; 1; ; N , where t = 0, tN = t + , and tn tn = h.
Consider the deltahedged gains over one period, from tn to tn . If the volatility risk premium
is zero, then from equation (13),
0 1 +1 Zn Cn = Cn + u dSu + +1 n +1 Zn +1 n vu dWu ; (35) 2 @
@
where, for brevity, we intend n to mean tn . Dene the operators, L[:] = @t [:] + t St @St [:] +
@
@
@
@
@
t @t [:] + t St @St [:] + t @t [:] + tStt @[email protected] @t [:], = tSt @St [:] and = t @t [:]. Appealing
to an ItoTaylor expansion,
1 2 2
2 2 2 Cn +1 1 = Cn +
+ 2
2 2 2 Zn +1 n 2 1 Zn Zu 2 Zu Zu L[t]dt +
[t]dSt +
[t]dt dSu
n
n
Zu
Zu
Zu
vn + L[vt] dt +
[vt ] dSt +
[vt]dt du :
+1 n n + 1 2 0 n n n 1 (36) 2 With an additional ItoTaylor expansion to include all terms up to O(h), we can rewrite this as, Cn Zn +1 Zn Zu
+1 Zn +1 = Cn + n
dSu + n Sn [n]
dWt dWu + vn
dWu
n
n
n
n
Zn Zu
Zn Zu
+ n Sn n [n ]
dWt dWu + n [vn]
dWt dWu +1 2 2 1 +1 2 + n Sn n [vn ]
2 Zn Zn
n
u
+1 n R n 2 1 2 1 1 +1 n 1 n dWt dWu + A ;
1 2 2 (37) 0 R 2 2 R R n
u
n
u
where A consists of terms such as n n g (St; t; t) dt du, and n n h(St ; t; t) dWtj ds; j =
S; . Under generally accepted regularity conditions (Lemma 2.2 of Milstein (1995)), E (A ) =
O(h ), and E (A ) = O(h ). It follows from Theorem 1.1 in Milstein (1995) that the order of
accuracy of the above discretization over the N steps in the interval, t = 0 to t = t + , is
h = =N , so that it is of O(1=N ). Rearranging (37), we can write t;t as,
0 +1 +1 0 2 2
0 3 + t;t + N
X 1 n
N
X Cn +1 Cn n (Sn = 1 n =0 [tn Stn [tn ]
2 2 Sn ); +1 =0 1 Zn Zu
+1 tn tn dWt dWu + vtn 30 1 1 Zn +1 tn dWt +
2 + tn Stn tn [tn ]
2 + tn Stn tn [vtn ] Zn Zu
+1 Z tt Z ts 2 n
n+1 n tn tn dWt dWu + tn [vtn ]
2 1 2 1 Zn Zu
+1 tn tn dWt dWs
2 2 dWt dWs ] + O(1/N):
1 (38) 2 As the expected value of the Ito integrals is zero, the proposition is proved. 2 Proof of Lemma 1: The proof is by induction. To x ideas, we prove the case where t( ). The
extension to t(t ; ) is straightforward. Consider L[t ( ) S ,
@ S + S @S + 1 S @ S
t @S
2t
@S
@
1 ( 1) ( ) S
=
@ t [email protected] + t( ) t + 2
t
t L[( ) S = 2 2 (39) 2 (40) 2 by assuming dSt = t St dt + t St dW . This implies that L[L[t ( )S ] is again proportional to
S . By induction, Ln [g], for any n 2 f1; 2; 3; :::g, are proportional to S . 2 Proof of Proposition 2: The proof relies on evaluating each term in the expansion of equation
(18). We have E(t;t ) = g (St; t) + L[g (St; t)] + L [g (St; t)] + , where g (St; t) =
1 + 1 2 2 3 2 6 t @[email protected]t. Here, the vega is proportional to St, and so @[email protected]t = t( ; y ) St.
Under the maintained assumption that t = t, g (St; t) = t( ; y ) St t . Whence, L [g] =
1 (@[email protected] ) St t + t St(@[email protected]) t + t St ( t )(@[email protected]t);
= ' St t;
1 (41)
(42) where ' @ [email protected] + . From Lemma 1, successive Ln [g ] inherit the same form as (42),
P
as in 'n St t . Therefore, E(t;t ) = 't( ) St t , where 't ( ) 1 nn 'n . 2
n
1 1+ + =0 1+ ! Proof of Equation 24: Using (23) and applying Ito's lemma, the call option satises the dynamics: Ct + Z t @Cu
Z t @Cu
Zt
= Ct +
dSu +
du +
bu du;
@Su
@u
t
t
t
Zt Z1
J
u
(C (Su ex ) C (Su )) q [x] dx du;
+ + + + t (43) 1 where C (Su ex ) implies that the option price is evaluated at Su ex . In (43), q [x] is the physical
C
C
@
density of the jumpsize, x, and bu @Cu + u Su @@Suu + u @@uu + u u Su @SuCu u . The call
@u
@
1 2 2 31 2 2 2 1
2 2 2 2 2 price is a solution to the partial integrodierential equation,
1 S @ C + 1 @ C + S @ C + (r ) S @C + ( [ ]) @C
t t @[email protected]
t
t
JJt
2 t @S 2 t @
@S
@
Z1
@C r C +
x ) C (S )) q [x] dx = 0;
(C (Su e
(44)
+ @t
Jt
u
2 2 2 2 2 2 2 2 1 for riskneutral density q [x]. Combining (43) and (44) and using the denition of t;t , we get
(24). 2
+ Expressions for RiskNeutral Skew and Kurtosis Used in Section 6: The modelfree estimates of riskneutral return skewness and kurtosis are based on Bakshi, Kapadia and Madan
(1999). Specically, the riskneutral skewness, SKEW (t; ), is given by n o Et (Rt;t Et[Rt;t ])
SKEW (t; ) n
o=
Et (Rt;t Et[Rt;t ])
r
r
= e W (t; ) r 3(t; )e V (t; ) + 2(t; )
[e V (t; ) (t; ) ] =
3 + + 2 + 32 + 3 (45) 232 and the riskneutral kurtosis, denoted KURT (t; ), is
r
r W
r
KURT(t; ) = e X (t; ) 4(t; )e r (t; ) + 6e (t; ) V (t; ) 3(t; ) ; (46)
[e V (t; ) (t; ) ]
2 4 22 where V (t; ) = K
Z 1 2(1 ln h S i)
St K t 2 C (t; ; K ) dK + Z S 2(1 + ln h S i)
K
t t K 0 2 P (t; ; K ) dK (47) and the price of the cubic and the quartic contracts are K
K
Z 1 6 ln h S i 3(ln h S i)
W (t; ) =
C (t; ; K ) dK
S
hS K
i
hS i
Z
2 t 2 t St 0 X (t; ) = t 6 ln Kt + 3(ln Kt )
P (t; ; K ) dK; Z 1 12(ln 2 hK i K 4(ln St )
C (t; ; K ) dK
K
St
Z St 12(ln h St i) + 4(ln h St i)
K
K
+
P (t; ; K ) dK:
K
St ) hK i 2 2 (48) 3 2 2 3 2 0 32 (49) Each security price can be formulated through a portfolio of options indexed by their strikes. In
addition, (t; ) er 1 er V (t; ) er W (t; ) er X (t; ). 2
2 6 24 Appendix B: Simulation Experiment
To implement the simulation experiment, the stock return and volatility process are discretized
as (h is some small interval): p = St + St h + t St t h;
p
= t + t h + t t h:
h St
t + h 1 2
+ 2 2 (50)
(51) 2 0,
Simulate a time series of two independent, standard normal processes: t ; t 0 where t =
0
1
01
1
1 A
t A = H @ t A. The
1; 2; ; T . Dene H = @
, and generate a new vector: @
1
t
t
transformed vector is a bivariate normal process with zero mean and a variancecovariance matrix
of H, where t and t have a correlation of . Construct the time series of St and t , t = 1; 2; ; T ,
based on equations (50)(51) and using the simulated t and .
The initial stock price is set to be 100, and the initial value of volatility is chosen to be 10%.
We initially assume that = 2:0, = 0:01, = 0:1, and =0.50.
For the calculations involving deltahedged gains, the riskneutralized variance process is:
1 2 1 1 1
2 2 1 2 2 1 2
2
p
= t + t h + t t h
h t 2
+ 2 2 (52) 2 where t[t ] = t , so that and are related to the physical parameters of the variance process
by the relations = + , and = =( + ).
Suppose we set = 0:20, then each path corresponds to 73 observations of fSt; tg. The deltahedged gains, t;t , over the period, , is calculated using equation (6), for h call of strike 100 and
a
n
R 1 Re e iu K f u i duo
initial maturity of 0.2 years. The call price is computed as: St +
u
n
R 1 Re h e iu K f u i duo, where the characteristic functions, f iand f , are disr
Ke
+
iu
played in Heston (1993, equation (17)). For simplicity, the interest rate and the dividend yield
are assumed to be zero. 2
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Econometrica 50, 10291084.
[16] Heston, S., 1993, \A closed form solution for options with stochastic volatility with applications to bond and currency options, Review of Financial Studies, 6(2), 327343.
[17] Hull, J., and A. White, 1987, \The pricing of options with stochastic volatilities," Journal of
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[18] Jackwerth, J., 2000, \Recovering risk aversion from options prices and realized returns,"
Review of Financial Studies 13 (2), 433451.
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35 t;t+ Table 1: DeltaHedged Gains for S&P 500 Index Calls = max(St+
K; 0)
Ct N1
X
n=0 tn (Stn+1
Stn ) N1
X
n=0 ( rn Ct
tn Stn ) N ; We compute the gain on a portfolio of a long position in a call option, hedged by a short position in the underlying stock, such that the net investment
earns the riskfree interest rate. The discretely rebalanced deltahedged gains, t;t+ , are computed as 1 N 2.5% to 5% 0% to 2.5% 4811 5530 5752 1430 3160 0.11
(0.01)
0.09
(0.00)
0.09
(0.00)
0.10
(0.01) All 2.93
(0.51)
0.31
(0.19)
0.93
(0.10)
1.03
(0.07) 97.37
(3.81)
70.71
(3.81)
34.43
(2.57)
6.98
(1.54) 4.44
(0.31)
1.34
(0.15)
0.62
(0.09)
1.51
(0.07) 56.34
(3.50)
38.13
(2.08)
11.88
(1.54)
7.95
(0.72) 1430 3160 3.88
(0.27)
0.96
(0.12)
0.74
(0.07)
1.33
(0.05) 68.30
(2.80)
48.05
(1.87)
20.19
(1.36)
7.59
(0.73) All Panel A: Full Sample Period 0.13
(0.01)
0.10
(0.01)
0.11
(0.01)
0.13
(0.01) 0.11
(0.01)
0.05
(0.01)
0.04
(0.00)
0.11
(0.00) (in %) All 0.06
(0.01)
0.06
(0.00)
0.06
(0.01)
0.06
(0.01) 0.13
(0.01)
0.07
(0.01)
0.03
(0.01)
0.12
(0.01) =C 1430 3160 0.31
(0.02)
0.28
(0.01)
0.32
(0.02)
0.42
(0.02) 0.07
(0.01)
0.01
(0.01)
0.05
(0.01)
0.08
(0.01) (in %) 0.37
(0.03)
0.32
(0.02)
0.38
(0.02)
0.52
(0.03) 0.45
(0.02)
0.19
(0.02)
0.20
(0.02)
0.47
(0.02) =S 0.18
(0.02)
0.19
(0.01)
0.21
(0.02)
0.26
(0.04) 0.55
(0.03)
0.26
(0.02)
0.18
(0.02)
0.54
(0.02) (in $) 0.29
(0.04)
0.05
(0.03)
0.24
(0.02)
0.36
(0.02) 26 41 60 68 68 72 78 87 1<0
% where the interest rate, rn, and the option delta, tn , are updated on a daily basis. The option delta is computed as the BlackScholes hedge ratio
evaluated at the GARCH volatility. The rebalancing frequency, =N , is set to one day. We report (i) the dollar deltahedged gains (t;t+ ), (ii)
the deltahedged gains normalized by the index level (t;t+ =St ) and (iii) the deltahedged gains normalized by the option price (t;t+ =Ct). All
deltahedged gains are averaged over their respective moneyness and maturity category. The moneyness of the option is dened as y Ser z =K .
The standard error, shown in parenthesis, is computed as the sample standard deviation divided by the square root of the number of observations.
1<0 is the proportion of deltahedged gains with < 0, and N is the number of options. Results are shown separately for options with maturity
1430 days and 3160 days; \All" combines the deltahedged gains from both maturities. There are 36,237 call option observations on the S&P 500
index. Subsample results are displayed in Panel B: SET 1 corresponds to 1988:011991:12, and SET 2 corresponds to 1992:011995:12 (standard
errors are small and omitted in Panel B). y Moneyness
10% to 7.5% 1284
7.5% to 5% 3619
5% to 2.5% 5684 5% to 7.5% 3647 2.5% to 0% 5903 7.5% to 10% 36 Panel B of Table 1: DeltaHedged Gains Across the 88:0191:12 and 92:0195:12 Subsamples 2.39 0.93 1.48
9.84 12.17 11.31 0.15 0.13 19.96 7.39 12.19
0.08 0.06 44.94 14.84 25.66 63
71 63
70 70
73 1<0
% 0.96
5.71 59
60 All 1.46
6.31 0.51
1.24 50
35 (in %) 0.09 0.08
0.15 0.12 0.10
4.69 0.83
1.65 0.39
0.96 38
16 =C 0.09
0.03 0.09 0.07
0.16 0.13 0.02
0.52 0.21
0.88 0.95
1.60 All 1430 3160 86
92 10% to 7.5% SET 1 0.45 0.39
0.34 0.28 0.06
0.07 0.06 0.04
0.07 0.05 0.68
1.10 1.12
1.76 (in %) 0.17 0.13 97.34 46.83 65.01
0.03 0.03 97.82 77.86 76.73 78
79 0.29
0.16
0.26 0.23
0.67 0.54 0.03
0.09 0.02
0.06 0.71
1.30 =S 0.06
0.02 0.16 0.12 68.44 18.98 38.60
0.05 0.04 75.88 54.62 58.85 SET 2 3375
0.18
0.31 0.28 0.20
0.72 0.61 0.01
0.02 0.00
0.05 0.07
0.13 All 1430 3160 0.47 0.36
0.14 0.13
0.07
0.03 2.5% to 0% SET 1 2240
SET 2 3668
0.08
0.42 0.19 0.12
0.31 0.22 0.04
0.07 0.09
0.14 (in $) N
0.18
0.10
0.45 0.36
0.20 0.18 SET 1 2211
SET 2 3540
0.02
0.08 0.06
0.29 0.06
0.10 Moneyness Sample
989
295
0.22
0.11 0% to 2.5% SET 1 2119
SET 2 3410 0.02
0.27 0.26
0.62 1430 3160 SET 2
1930 1 SET 2 1688 y 7.5% to 5% SET 1 2.5% to 5% 0.14
0.31 0.30
0.69 2308 5% to 7.5% SET 1 1882
SET 2 2928 0.19
0.50 5% to 2.5% SET 1 7.5% to 10% SET 1 1523
SET 2 2123 37 Table 2: DeltaHedged Gains for OutofMoney Puts
This table reports the deltahedged gains for outofmoney puts on the S&P 500 index. Put options correspond to moneyness, y, greater than 1. We compute the gain on a portfolio of a long
position in a put option, hedged by a short position in the underlying stock, such that the net
investment earns the riskfree interest rate. As before, the discretely rebalanced deltahedged gains,
t;t+ , are computed as:
t;t+ = max(K St+ ; 0) Pt N1
X
n=0 ^
t (St +1
n n Stn ) N1
X
n=0 rn Pt ^
t n Stn
N ^
where t is the BlackScholes put option delta evaluated at GARCH volatility; and rn is the
nominal interest rate. The rebalancing frequency, =N , is set to one day. Reported are (i) dollar
deltahedged gains (t;t+ ), and (ii) deltahedged gains normalized by the put price (t;t+ =Pt). All
deltahedged gains are averaged over their respective moneyness and maturity categories. 1<0 is
the proportion of deltahedged gains with < 0. N represents the number of put options. There
are 20,216 outofmoney puts. SET 1 refers to the 1988:011991:12 subsample, and SET 2 refers
to the 1992:011995:12 subsample. Standard errors are small, and omitted.
n Moneyness Sample N
y 1 (in $) =P (in %) 1430 3060 All 1430 3160 1<0 All % 0% to 2.5% FULL 5342
SET 1 2116
SET 2 3226 0.55
0.16
0.79 0.77 0.69 16.95 13.01 14.47
0.10 0.00 0.06 4.82 3.03
1.27 1.09 27.10 23.27 24.68 74
62
81 2.5% to 5% FULL 5695
SET 1 2011
SET 2 3684 0.80
0.66
0.88 1.37 1.16 55.40 41.72 46.82
0.80 0.75 23.83 13.95 17.63
1.68 1.38 72.61 56.89 62.75 89
81
94 5% to 7.5% FULL 5364
SET 1 2042
SET 2 3322 0.60
0.66
0.56 1.30 1.03 73.57 66.82 69.41
1.18 0.97 45.06 39.23 41.56
1.38 1.07 92.25 83.11 86.53 94
88
98 FULL 3815
7.5% to 10% SET 1 1568
SET 2 2247 0.43
0.57
0.33 1.06 0.82 91.59 82.66 86.14
1.12 0.90 81.72 60.90 69.17
1.02 0.76 98.71 97.54 97.99 97
94
99 38 Table 3: DeltaHedged Gains for Near Money Calls, by Volatility Regimes
Each date is classied into one of seven dierent volatility regimes based on the annualized estimate
of historical volatility (i.e., VOLh). Deltahedged gains are computed as described in the note to
Table 1. We report (i) the dollar deltahedged gains, (ii) deltahedged gains normalized by the index
price, and (iii) the deltahedged gains normalized by the call price, averaged over each volatility
regime. Both the mean and the median are displayed. The results are reported for two near money
categories: 2.5% to 0% and 0 to 2.5%. The standard errors are small and therefore omitted. T
represents the number of days in each volatility classication.
y VOLh(%) T
(%)
<8
428 Mean 2 [2.5%,0]
=S ($) (%)
0.11 0.04
Median 0.21 0.04 =C y
2[0,2.5%]
=S =C (%) ($) (%)
5.64 0.25 0.04
7.81 0.38 0.08 (%)
1.69
3.87 399 Mean 0.54 0.10 13.52 0.37 0.06
Median 0.72 0.16 19.63 0.57 0.13 3.03
5.92 10 to 12 450 Mean 3.02
4.92 12 to 14 295 Mean 1.78
4.27 14 to 16 147 Mean 1.01 0.29 20.90 1.14 0.32 11.97 8 to 10 0.35 0.07 6.68 0.38 0.08
Median 0.54 0.14 13.18 0.51 0.13
0.44 0.11 8.52 0.22 0.06
Median 0.50 0.14 11.28 0.41 0.11 Median 1.12 0.31 21.84 1.13 0.32 12.33 16 to 18 99
> 18 Mean 1.32 0.41 24.93 1.31 0.40 12.87
Median 1.38 0.42 27.36 1.28 0.38 12.34 125 Mean 1.57 0.51 22.90 1.51 0.47 13.41 Median 1.42 0.48 25.53 1.62 0.51 14.58 39 Table 4: DeltaHedged Gains and Option Vega: CrossSectional Tests
i
i
= 0 + 1 VEGAt + t ;
= ut + !ti ; We estimate a FGLS random eect panel regression of deltahedged gains on the VEGA:
i
GAINSt
i
t 0.029
(0.53) 0.018
(0.26) 0.046
(1.22) 0.057
(1.65) 0 0.53
[10.83] 0.19
[3.94] 0.06
[1.02] 0.18
[4.15] 0.10
[2.95] 1 0.054
[0.75] 0.142
[3.32] 0.068
[1.81] 78 65 0.450 6.00 55
[5.67] [10.83] 0.179
[3.25] 3.88
[1.79] 0.704 6.78
[12.66] [8.21] 0 0.421
[1.96] 0.283
[1.95] 0.025
[0.13] 2.44 125 0.062
[4.16]
[0.81] 1.02 219 0.070
[1.27]
[0.75] 2.73 247 0.195
[4.60]
[2.38] 2.68 300 0.067
[7.35]
[1.72] N i
i
2
where GAINSt t;t+ =St (for strike Ki , i; ; I ). We use two proxies for the option vega. First, VEGA is dened as exp( d1), where d1 p
1
1
p
log(y) + 2 , where yi is option moneyness corresponding to strike Ki . Second, VEGA is the absolute level of moneyness, jy 1j. The data
consists of monthly observations of calls over the period January 1988 to December 1996. We focus on options with maturity of 30 and 44 days. The
sample is chosen such that the volatility is within a predened interval, VOLh . N is the number of observations. Since the estimation method is not
least squares, the coecient of determination is omitted. Numbers in square brackets show the zstatistics (Greene (1997)). 241 N 44 Days Options 8 0.129
(1.61) 0.67
[8.69] 0.586
[2.27] 0.86
[4.15] 0.66
[5.90] 0.29
[1.38] 0.41
[8.35] 0.14
[2.05] 0.36
[10.57] 0.03
[0.86] 0.522
[3.59] 0.443
[3.45] 0.292
[2.04] 0.387
[5.27] 0.079
[0.87] 5.64
[6.01] 2.70
[1.34] 4.46
[8.52] 1.65
[1.98] 0.182 4.84
[2.21] [11.17] 0.001
[0.23] 5.22
[4.29] 30 Days Options
Vega is exp( < 212 0.029
(0.47) 0.47
[1.69] Vega is exp( d2
Vega is jy 1j
1 =2) 1 0 1 1012 283 810 83 1214 177 VOLh 0 1416
49 0.052
(0.17) 2) Vega is jy 1j 1 1618 46 2
d1= 18 1.69
[3.49] > 40 Table 5: Robustness Results, DeltaHedged Gains and Option Vega (30 days Calls)
In Panel A, we report the results from the following FGLS random eect panel regression: GAINSit =
i 0 + 1 VOLh VEGAit + it, and it = ut + !ti . The dependent variable is GAINSit t;t+ =St
t
1
2 ) or jy 1j, where d1 = p log(y )+ 1 p . As before,
(i; ; I ). VEGA is dened as either exp( d1
2
y is the option moneyness. The data consists of monthly observations of calls over the period January 1988 to December 1995. N is the number of observations. Numbers in square brackets show
zstatistics (Greene (1997)). In Panel B, we repeat the analysis of Table 4 for the 1992:011995:12
subsample. All results are for 30 day calls.
Panel A: The Specication is, GAINSit = 0 + 1 VOLh VEGAit + it
t
VEGA is exp( d2) VEGA is jy 1j
1
h (%)
VOL
N 0 1 0 1
8 < 810
1012
1214
1416
1618
> 18 158 0.087
[2.08]
212 0.040
[1.04]
283 0.26
[0.37]
177 0.026
[0.47]
83 0.129
[1.60]
49 0.024
[0.34]
44 0.061
[0.20] 0.22
[3.93]
0.18
[3.77]
0.07
[1.27]
0.14
[3.82]
0.36
[10.97]
0.39
[8.60]
0.21
[1.63] 0.073
4.30
[1.58] [5.86]
0.136
2.81
[3.30] [4.35]
0.060
1.09
[0.85] [1.48]
0.177
1.86
[3.22] [4.07]
0.450
4.02
[5.70] [10.98]
0.701
3.89
[13.24] [8.18]
0.589
1.82
[2.33] [1.85] Panel B: Subsample Results for 1992:011995:12
VEGA is exp( d2) VEGA is jy 1j
1
h (%)
VOL
N 0 1 0 1
8 < 810
1012
1214 158 0.084
[1.97]
184 0.049
[1.43]
148 0.013
[0.28]
177 0.029
[0.53] 0.13
[3.55]
0.26
[4.15]
0.27
[7.02]
0.19
[3.94] 41 0.071
[1.55]
0.210
[5.78]
0.293
[6.24]
0.179
[3.25] 2.82
[5.60]
3.73
[8.85]
3.60
[7.01]
2.44
[4.16] Table 6: Changes in Valuation of Index Options During Large Moves
We proxy valuation changes in index calls by the corresponding change in BlackScholes implied
volatility. This is done in two steps. First, on the day prior to a large daily move, we buy a call
option and compute the BlackScholes implied volatility. Second, proceeding to the day after the
large move, we recompute the BlackScholes implied volatility. We report four set of numbers (i)
the price movement (in %) (ii) the prior day implied volatility (denoted as \Prior IMPL."), (iii) the
subsequent day implied volatility (denoted as \Subs. IMPL."), and (iv) the corresponding change
in implied volatility as a fraction of the implied volatility of the option bought (i.e., the relative
change). The sample period is 1988 through 1995. In each implied volatility calculation, the index
level is adjusted by the present discounted value of dividends. Only shortterm call options with
strikes that are closest to atthemoney are considered. We display the results from the largest
20 percentage price movements. Largest Negative Price
Movements
Date Largest Positive Price
Movements Price Prior Subs. Relative Date Price Prior Subs. Relative
Move IMPL. IMPL Change
Move IMPL. IMPL Change 880108
891013
880414
911115
900823
900806
880120
901009
900122
900112
930216
910819
890317
900816
940204
900924
881111
880324
900821
910510 7.00
6.32
4.44
3.73
3.04
3.00
2.72
2.70
2.61
2.48
2.41
2.37
2.27
2.26
2.24
2.14
2.13
2.09
2.03
1.96 26.69
13.23
20.05
12.31
25.90
20.84
24.14
24.83
17.16
17.00
11.20
13.55
14.22
19.76
9.14
24.37
18.15
21.35
24.21
14.02 28.90
11.87
21.02
15.90
28.96
23.01
25.71
24.15
19.61
18.02
15.41
16.04
15.69
24.11
11.10
23.70
18.61
23.69
25.90
14.86 8.26
10.32
4.84
29.16
11.83
10.37
6.51
2.75
14.26
6.00
37.57
18.42
10.30
21.98
21.50
2.77
2.50
10.94
6.99
6.01 910117
880531
900827
901001
910821
891016
880406
910211
911223
880115
880728
880608
880902
900511
880125
901018
880729
890512
910402
901019 Avg. 3.00 18.61 20.31 10.58 Avg. 42 3.66
3.41
3.18
2.90
2.90
2.75
2.66
2.58
2.50
2.49
2.39
2.37
2.37
2.37
2.31
2.31
2.24
2.22
2.19
2.18 27.69
19.21
28.96
23.27
16.04
11.87
20.16
19.07
13.99
29.03
17.27
20.45
19.06
16.60
25.10
25.18
17.58
14.30
16.24
25.25 21.29
19.25
24.21
21.61
13.05
20.48
19.36
19.58
12.80
25.38
18.79
21.17
17.40
16.93
25.38
24.26
17.48
14.27
16.43
20.40 23.11
0.22
16.40
7.15
18.67
72.60
3.98
2.69
8.49
12.59
8.80
3.57
8.69
1.96
1.10
3.65
0.59
0.16
1.22
19.19 2.60 20.32 19.48 1.53 Table 7: DeltaHedged Gains and Volatility Risk Premium: TimeSeries Regressions
The regression results are based on the following specication for deltahedged gains and realized volatility:
GAINSt = 0 + 1 VOLt + 2 GAINSt 1 + et ;
where GAINSt t;tt+ . VOLt represents the prior month realized volatility. The null hypothesis is that
S 1 = 0. We include a lagged dependent variable to correct for the serial correlation of the residuals
(the CochraneOrcutt procedure yields similar inferences). The table reports the coecient estimate, the
tstatistic (in square brackets), the adjusted R2 , and the BoxPierce statistic with 6 lags (denoted Q6 ).
The pvalues for Q6 are in parenthesis. The tstatistics are based on the NeweyWest procedure with a
lag length of 12. FULL refers to the entire sample period of 1988:011995:12; SET 1 corresponds to
the subsample of 88:191:12; and SET 2 corresponds to the subsample of 92:0195:12. The results are
reported for closest to atthemoney calls (with average moneyness of 1.004). For comparison, the
regressions are performed using both the historical volatility, VOLh , and the GARCH volatility, VOLg .
t
t
The GARCH estimation employs one year of daily return observations (for each of the 8 years from 1988
through 1995). All regressions use call options sampled monthly, with a constant maturity of 30 days, 44
days and 58 days, respectively. Historical Volatility, VOLh
t
Sample 30 FULL
Days
SET 1
SET 2
44 FULL
Days
SET 1
SET 2
58 FULL
Days
SET 1
SET 2 0
(10 2 ) 1 0.22
[2.00]
0.87
[2.09]
0.32
[2.45] 0.032
[4.39]
0.073
[2.66]
0.051
[3.92] 0.38
[2.53]
1.01
[4.37]
0.52
[4.31]
0.40
[1.92]
1.24
[3.07]
0.49
[4.69] 2 R2 GARCH Volatility, VOLg
t 1 0.199 10.80 1.78
[3.47]
(0.94)
0.137 15.35 1.10
[1.36]
(0.98)
0.058 14.70 4.45
[0.55]
(0.62) 0.05
[0.41]
1.36
[2.04]
0.69
[1.62] 0.017
[1.71]
0.101
[1.07]
0.089
[1.97] 0.282 6.74 2.34
[6.04]
(0.88)
0.361 12.55 1.91
[7.77]
(0.93)
0.067 3.81 4.20
[0.71]
(0.65) 0.045
[4.27]
0.080
[5.34]
0.070
[5.23] 0.125 13.87 1.68
[1.70]
(0.95)
0.077 24.75 3.06
[0.96]
(0.80)
0.422 36.22 6.52
[7.04]
(0.37) 0.15
[1.13]
1.91
[4.03]
0.99
[4.40] 0.023
[2.23]
0.135
[4.49]
0.118
[4.97] 0.073 0.01 3.37
[1.09]
(0.76)
0.093 13.00 5.43
[1.23]
(0.49)
0.317 24.21 9.34
[7.21]
(0.16) 0.048
[3.50]
0.099
[4.21]
0.066
[6.57] 0.217 11.81 2.81
[1.45]
(0.83)
0.132 19.00 2.31
[0.71]
(0.89)
0.510 36.56 2.18
[10.12]
(0.90) 0.03
[0.17]
1.82
[2.40]
1.06
[2.83] 0.013
[0.89]
0.131
[2.76]
0.126
[3.18] 0.199 2.49 3.09
[1.49]
(0.80)
0.022 14.65 2.98
[0.15]
(0.81)
0.311 24.79 2.30
[9.59]
(0.88) 43 2 R2 0
(10 2 ) (%) Q6 (%) Q6 Table 8: Properties of DeltaHedged Gains in Simulated Economies Simulated DeltaHedged Gains
=C Simulated Coecient Values 0.0050 0.0050 0.0120 0.0121 0.0710 0.0710 0.0420 0.0394 GAINSt = 0 + 1 VOLt + 2 GAINSt 1 + et
Freq. for Freq. for
t(
1) > 2 t(
1) < 2
R2 4.90% 4.90% 2.44% 2 0.149 0.149 0.020 1 0.615 0.616 0.555 2.43% 0.077 0.077 0.055 0.019 f0.170g f1.412g f0.093g f1.74g
0.2039 0.1372 0.5151 0.519 f0.1358g f0.171g f1.407g f0.093g f1.73g
0.0017 0.0021 (%)
0.1641 Hedge
=S
Ratio
($)
(%)
SV
0.0024 0.0018
f0.0235g f0.0015g f0.1346g 0.0022 0.0089 0.0022 f0.137g f0.922g f0.092g f3.17g SV
BS 0.0036 f0.0233g f0.0015g f0.1629g f0.136g f0.923g f0.093g f3.17g BS f0.0305g f0.0021g f0.1576g 0.046 0 We simulate deltahedged gains in an economy where volatility is stochastic, but not priced. The simulation experimentp Appendix
(see
1
B for more details) is based on the following discretization of stock returns and volatility: St+h = St + St h + t St t h and t2+h =
2
2
2p
t +
t h + t t h, where h is set equal to 1 day. For the simulation S0 = 100, 0=10%, = 2, = 0:01, = 0:5, and
= 0:1. In each simulation run, the theoretical call option values are generated according to the stochastic volatility option pricing model
of Heston (1993). Throughout, we assume that interest rate and the dividend yield are zero, and [t] = 0. The deltahedged gains are
computed in two dierent ways. First, we use the hedge ratio given by the stochastic volatility model (denoted as \SV"), and second
using the BlackScholes model (denoted as \BS"). We consider atthemoney options with a maturity of 30 days (and 44 days). Every 30
(44) days, the option is bought and deltahedged. Over 8 years (2880 days), this produces 96 monthly observations on deltahedged gains
and prior 30 days volatility. For each simulation, we perform the timeseries regression: GAINSt = 0 + 1 VOLt + 2 GAINSt 1 + et .
The reported 0 , 1 , 2, and adjusted R2 are averages over 1000 simulations. The mean absolute deviation of the estimate is shown
in curly brackets. We report the frequency of signicant 1 (i.e., t(
1 ) > 2 and t(
1 ) < 2). We also show the mean deltahedged gains
across all simulations, for both the SV model and the BS model. 30 Days 44 Days f0.0297g f0.0015g 44 Table 9: Eect of Jumps on DeltaHedged Gains
We employ skewness and kurtosis of the riskneutral distribution as proxies for jumpfear. The regression
results are based on the following specication between deltahedged gains, historical volatility, and the
higherorder moments of the riskneutral return distribution:
GAINSt = + VOLh + GAINSt 1 + SKEW + KURT + et ;
0
1
t
2
3
t
4
t
where GAINSt t;tt+ . VOLh represents the historical volatility. To correct for the serial correlation of
t
S
the residuals, we have included a lagged dependent variable (the CochraneOrcutt procedure yields similar
inferences). We record the coecient estimate, the tstatistic (in square brackets), the adjusted R2 , and
the BoxPierce statistic with 6 lags (denoted Q6 ). The pvalues for Q6 are in parenthesis. The tstatistics
are based on the NeweyWest procedure with a lag length of 12. FULL refers to the entire sample period
of 1988:011995:12; SET 1 corresponds to the subsample of 88:191:12; and SET 2 corresponds to the
subsample of 92:0195:12. The results are reported for closest to atthemoney calls. All regressions
use call options sampled monthly, with a constant maturity of 30 days, 44 days and 58 days, respectively.
The modelfree estimate of riskneutral skewness, SKEW, and the riskneutral kurtosis, KURT , are
t
t
constructed as described in the Appendix. Sample
0
(10 2 )
30 FULL 0.51
Days
[2.86]
SET 1 1.18
[1.97]
SET 2 0.63
[3.55]
44 FULL
Days
SET 1
SET 2
58 FULL
Days
SET 1
SET 2
1
2 0.041
[3.44]
0.082
[1.99]
0.064
[4.74] 0.16
[2.84]
0.10
[0.94]
0.04
[0.47] 0.45
[2.03]
1.11
[2.84]
0.55
[3.34] 0.046
[4.09]
0.085
[3.70]
0.068
[3.95] 0.19
[3.31]
0.118
[1.43]
0.42
[7.01] 0.23
[1.51]
0.04
[0.23]
0.12
[0.70] 0.32
[2.24]
0.05
[0.28]
0.17
[0.59] 17.26 0.60
[1.85]
1.71
[3.30]
0.46
[3.88] 0.055 0.22
[3.08] [1.30]
0.111 0.10
[3.35] [0.53]
0.064 0.50
[6.88] [7.04] 0.34
[1.46]
0.50
[3.14]
0.05
[0.43] 0.41
[1.79]
0.44
[4.25]
0.08
[0.55] 12.42 45
R2
Q6
3
4
2 ) (10 3)
(10
0.31
0.28 13.98 1.26
[1.82]
[1.73]
(0.97)
0.21
0.13 15.46 1.27
[0.90]
[0.58]
(0.97)
0.17
0.05 17.31 4.52
[1.40]
[0.28]
(0.61)
2.41
(0.91)
23.47 3.36
(0.76)
34.13 6.41
(0.38)
1.82
(0.94)
19.03 2.55
(0.86)
35.41 3.71
(0.72) Table 10: DeltaHedged Gains for a PreCrash Period (January 1987 through June
1987, Calls and Puts)
For a selected precrash period, we display the deltahedged gains for outofmoney calls (i.e.,
y < 1) and outofmoney puts (i.e., y > 1). In the case of calls, the discretely rebalanced deltahedged gains, t;t+ , is computed as: t;t+ = max(St+ K; 0) Ct PN=01 t (St +1 St )
n
PN 1
t St ) N , where the interest rate, rn, and the option delta, t , are updated on a
n=0 rn (Ct
daily basis. The option delta is computed as the BlackScholes hedge ratio evaluated at the GARCH
volatility. The rebalancing frequency, =N , is set to one day. Reported in the table are (i) dollar
deltahedged gains (t;t+ ) and the (ii) deltahedged gains normalized by the index level (t;t+ =St).
All deltahedged gains are averaged over their respective moneyness and maturity category. The
standard errors, shown in parenthesis, are computed as the sample standard deviation divided by
the square root of the number of observations. 1<0 is the proportion of deltahedged gains with
< 0, and N is the number of options (in curly brackets). \All" combines the deltahedged gains
from maturities of 1430 days and 3160 days.
n n n n n Moneyness (in $)
=S (in %)
1<0
1
1430 3160 All 1430 3160 All fN g
10% to 7.5% 0.12 1.16 0.73 0.04 0.40 0.26 86%
(0.08) (0.23) (0.18) (0.03) (0.08) (0.06) f21g
y 7.5% to 5% 0.28 0.77 0.61 0.10 0.27 0.22
(0.10) (0.19) (0.14) (0.04) (0.07) (0.05) 69% f85g Calls 5% to 2.5% 0.64 1.11 0.96 0.22 0.38 0.33 73%
(0.11) (0.16) (0.11) (0.04) (0.06) (0.04) f238g 2.5% to 0%
0% to 2.5% 0.63 1.03 0.87 0.22 0.36 0.31 84%
(0.11) (0.10) (0.08) (0.04) (0.03) (0.03) f274g 5% to 7.5% 0.23 0.74 0.53 0.09 0.26 0.19 84%
(0.09) (0.07) (0.06) (0.03) (0.02) (0.02) f245g 7.5% to 10% ALL 0.82 0.87 0.85 0.29 0.31 0.30 74%
(0.15) (0.14) (0.10) (0.05) (0.05) (0.03) f282g 2.5% to 5% Puts 0.65 0.81 0.75 0.22 0.28 0.25 67%
(0.12) (0.14) (0.10) (0.04) (0.05) (0.03) f276g 0.07 0.67 0.44 0.03 0.24 0.16 92%
(0.07) (0.04) (0.04) (0.02) (0.01) (0.01) f150g
0.25 0.44 0.37 46 0.09 0.15 0.13 n ...
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