Bayesian Option Pricing Using Mixed Normal Heteroskedasticity Models

Jeroen Rombouts and Lars Stentoft

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

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.

Keywords: Bayesian inference; option pricing; finite mixture models; out-of-sample prediction; GARCH models (search for similar items in EconPapers)
JEL-codes: C11 C15 C22 G13 (search for similar items in EconPapers)
Pages: 49
Date: 2009-02-13
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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https://repec.econ.au.dk/repec/creates/rp/09/rp09_07.pdf (application/pdf)

Related works:
Journal Article: Bayesian option pricing using mixed normal heteroskedasticity models (2014)
Working Paper: Bayesian Option Pricing Using Mixed Normal Heteroskedasticity Models (2009)
Working Paper: Bayesian option pricing using mixed normal heteroskedasticity models (2009)
Working Paper: Bayesian Option Pricing Using Mixed Normal Heteroskedasticity Models (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2009-07

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