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Bayesian Option Pricing Using Mixed Normal Heteroskedasticity Models

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  • 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.)

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.

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Bibliographic Info

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

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: Bayesian inference; option pricing; finite mixture models; out-of-sample prediction; GARCH models;

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References

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Citations

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Cited by:
  1. Jeroen V.K. Rombouts & Lars Stentoft, 2010. "Multivariate Option Pricing with Time Varying Volatility and Correlations," Cahiers de recherche 1020, CIRPEE.
  2. BAUWENS, Luc & HAFNER, Christian & LAURENT, Sébastien, 2011. "Volatility models," CORE Discussion Papers 2011058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  3. Yin-Wong Cheung & Sang-Kuck Chung, 2011. "A Long Memory Model with Normal Mixture GARCH," Computational Economics, Society for Computational Economics, vol. 38(4), pages 517-539, November.

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