Option Valuation with Normal Mixture GARCH Models

Alex Badescu, Reg Kulperger and Emese Lazar
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Alex Badescu: University of Calgary
Reg Kulperger: University of Western Ontario
Emese Lazar: ICMA Centre, The University of Reading

Studies in Nonlinear Dynamics & Econometrics, 2008, vol. 12, issue 2

Abstract: The class of mixture GARCH models introduced by Haas, Mittnik and Paollela (2004) and Alexander and Lazar (2006) provides a better alternative for fitting financial data than various other GARCH models driven by the normal or skewed t-distribution. In this paper we propose different option pricing methodologies when the underlying stock dynamic is modeled by an asymmetric normal mixture GARCH model with K volatility components. Since under GARCH models the market is incomplete there are an infinite number of martingale measures one can use for pricing. For our mixture setting we analyze the impact of three risk-neutral candidates: a generalized local risk neutral valuation relationship, an Esscher transform and an extended Girsanov principle. We investigate the out-of-sample performance of an asymmetric GARCH model with a mixture density of two normals for Call options written on the S&P 500 Index. The performance under all three transformations is quite impressive when compared to the benchmark GARCH model with normal driving noise. The overall improvement is explained not only by the skewness and leptokurtosis exhibited by the innovation mixture distribution, but also by the richer parametrization used in modeling the dynamics of the multi-component conditional volatility.

Keywords: option pricing; GARCH; extended Girsanov principle; Esscher transform (search for similar items in EconPapers)
JEL-codes: G13 C22 (search for similar items in EconPapers)
Date: 2008

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Persistent link: http://EconPapers.repec.org/RePEc:bpj:sndecm:v:12:y:2008:i:2:n:5

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