Journal Article

American Option Pricing Using GARCH Models and the Normal Inverse Gaussian Distribution

Lars Stentoft

in Journal of Financial Econometrics

Volume 6, issue 4, pages 540-582
Published in print January 2008 | ISSN: 1479-8409
Published online September 2008 | e-ISSN: 1479-8417 | DOI: http://dx.doi.org/10.1093/jjfinec/nbn013
American Option Pricing Using GARCH Models and the Normal Inverse Gaussian Distribution

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In this paper we propose a feasible way to price American options in a model with time-varying volatility and conditional skewness and leptokurtosis, using GARCH processes and the Normal Inverse Gaussian distribution. We show how the risk-neutral dynamics can be obtained in this model, we interpret the effect of the risk-neutralization, and we derive approximation procedures which allow for a computationally efficient implementation of the model. When the model is estimated on financial returns data the results indicate that compared to the Gaussian case the extension is important. A study of the model properties shows that there are important option pricing differences compared to the Gaussian case as well as to the symmetric special case. A large scale empirical examination shows that our model out-performs the Gaussian case for pricing options on the three large US stocks as well as a major index. In particular, improvements are found when it comes to explaining the smile in implied standard deviations.

Keywords: C22; C53; G13; American options; GARCH models; least squares Monte Carlo method; normal inverse Gaussian distribution

Journal Article.  15373 words.  Illustrated.

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