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Title: Bayesian Option Pricing Using Mixed Normal Heteroskedasticity Models
Authors: Rombouts, Jeroen V.K.
Stentoft, Lars
Keywords: Bayesian inference
option pricing
finite mixture models
out-of-sample prediction
GARCH models
Issue Date: 2009-08
Series/Report no.: Cahiers du CIRPÉE;09-26
Abstract: 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.
Appears in Collections:Cahiers de recherche du CIRPÉE

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