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Bayesian Option Pricing Using Mixed Normal Heteroskedasticity Models

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Author Info
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.)

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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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Publisher 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
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Handle: RePEc:aah:create:2009-07

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

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

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Find related papers by JEL classification:
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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