Review of Derivatives Research, 2016.
The Homoscedastic Gamma [HG] model for the distribution of returns is characterized by its mean, variance and an independent skewness parameter. We check the model’s implications for option pricing. The information content of skewness leads to improved in-sample, out-of-sample pricing and hedging performances. Our results imply that expanding around the Gaussian density is restrictive and does not offer sufficient flexibility to match the skewness and kurtosis implicit in option data. The HG model predicts that the spread between historical and risk-neutral volatilities is a function of the risk premium and of skewness. We measure skewness from option prices and test these predictions. We find that conditioning on skewness increases the predictive power of the volatility spread and that coefficient estimates accord with theory.