Bayesian Option Pricing Using Mixed Normal Heteroskedasticity Models
51 Pages Posted: 12 Dec 2008 Last revised: 24 Feb 2009
Date Written: February 17, 2009
While stochastic volatility models improve on the option pricing error when compared to the Black-Scholes-Merton model, mispricings remain. This paper uses mixed normal heteroskedasticity models to price options. Our model allows for significant negative skewness and time varying higher order moments of the risk neutral distribution. Parameter inference using Gibbs sampling is explained and we detail how to compute risk neutral predictive densities taking into account parameter uncertainty. When forecasting out-of-sample options on the S&P 500 index, substantial improvements are found compared to a benchmark model in terms of dollar losses and the ability to explain the smirk in implied volatilities.
Keywords: Bayesian inference, option pricing, finite mixture models, out-of-sample prediction, GARCH models
JEL Classification: C11, C15, C22, G13
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