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Bayesian Option Pricing Using Mixed Normal Heteroskedasticity Models

  • Jeroen V.K. Rombouts

    ()

    (Institute of Applied Economics at HEC Montreal, CIRANO, CIRPEE, Universit´e catholique de Louvain (CORE).)

  • Lars Stentoft

    ()

    (Department of Finance at HEC Montreal, CIRANO, CREATES, CREF.)

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.

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Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2009-07.

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Length: 49
Date of creation: 13 Feb 2009
Date of revision:
Handle: RePEc:aah:create:2009-07
Contact details of provider: Web page: http://www.econ.au.dk/afn/

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