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Option Returns and the CrossSectional
Predictability of Implied Volatility
*
Alessio Saretto
The Krannert School of Management
Purdue University
†
November 2005
‡
Abstract
I study the crosssection of realized stock option returns and Fnd an economically
important source of predictability in the crosssectional distribution of implied
volatility. A zerocost trading strategy that is long in straddles with a large posi
tive forecast of the change in implied volatility and short in straddles with a large
negative forecast produces an economically important and statistically signiFcant
average monthly return. The results are robust to diﬀerent market conditions, to
Frm riskcharacteristics, to various industry groupings, to options liquidity charac
teristics, and are not explained by linear factor models. Compared to the market
prediction, the implied volatility estimate obtained from the crosssectional fore
casting model is a more precise and eﬃcient estimate of future realized volatility.
*
I thank Raﬀaella Giacomini, Richard Roll, Pedro SantaClara, and Walter Torous for their constant
support, and Laura Frieder, Robert Geske, Amit Goyal, Mark Grimblatt and seminar partecipants at
UCLA for valuable suggestions.
†
West Lafayette, IN, 47907, phone: (765) 4967591, email: asaretto@purdue.edu.
‡
The latest draft is available at: http://www.krannert.purdue.edu/faculty/saretto/.
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Introduction
Volatility is central to the pricing of options as there is a onetoone correspondence
between the price of an option and the volatility of the underlying asset. In the context of
Black and Scholes (1973), given the option price it is possible to obtain an estimate of the
volatility by inverting the pricing formula. The resulting estimate is generally referred to
as the implied volatility, which represents the market’s estimate of the underlying future
volatility over the life of the option. Options are essentially bets on volatility because
an accurate prediction of future volatility delivers important economic information to
traders. It is, therefore, not surprising that there is an extensive literature on predicting
volatility. Granger and Poon (2003), for instance, survey the extant literature and
broadly Fnd that the market’s forecast embedded in implied volatility is the best forecast
of future volatility. However, this literature focuses mainly on predicting volatility of a
single asset (frequently the S&P 500 index) using
timeseries
methods. I show that there
is important information in the
crosssection
of stock volatilities that leads to better
forecasts of future volatility than those contained in the individual implied volatility
itself. To the best of my knowledge, this is the Frst paper to study the predictability of
the crosssection of individual equity option implied volatilities.
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 Spring '12
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