Pricing stock options under stochastic volatility and interest rates with efficient method of moments estimation
While the stochastic volatility (SV) generalization has been shown to improve the explanatory power over the Black-Scholes model, empirical implications of SV models on option pricing have not yet been adequately tested. The purpose of this paper is to first estimate a multivariate SV model using the efficient method of moments (EMM) technique from observations of underlying state variables and then investigate the respective effect of stochastic interest rates, systematic volatility and idiosyncratic volatility on option prices. We compute option prices using reprojected underlying historical volatilities and implied stochastic volatility risk to gauge each model’s performance through direct comparison with observed market option prices. Our major empirical findings are summarized as follows. First, while theory predicts that the short-term interest rates are strongly related to the systematic volatility of the consumption process, our estimation results suggest that the short-term interest rate fails to be a good proxy of the systematic volatility factor; Second, while allowing for stochastic volatility can reduce the pricing errors and allowing for asymmetric volatility or leverage effect does help to explain the skewness of the volatility smile, allowing for stochastic interest rates has minimal impact on option prices in our case; Third, similar to Melino and Turnbull (1990), our empirical findings strongly suggest the existence of a non-zero risk premium for stochastic volatility of stock returns. Based on implied volatility risk, the SV models can largely reduce the option pricing errors, suggesting the importance of incorporating the information in the options market in pricing options; Finally, both the model diagnostics and option pricing errors in our study suggest that the Gaussian SV model is not sufficient in modeling short-term kurtosis of asset returns, a SV model with fatter-tailed noise or jump component may have better explanatory power.
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