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Likelihood-Related Estimation Methods and Non-Gaussian GARCH Processes

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Abstract

This article discusses the finite distance properties of three likelihood-based estimation strategies for GARCH processes with non-Gaussian conditional distributions : (1) the maximum likelihood approach ; (2) the Quasi maximum Likelihood approach ; (3) a multi-steps recursive estimation approach (REC). We first run a Monte Carlo test which shows that the recursive method may be the most relevant approach for estimation purposes. We then turn to a sample of SP500 returns. We confirm that the REC estimates are statistically dominating the parameters estimated by the two other competing methods. Regardless of the selected model, REC estimates deliver the more stable results.

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File URL: ftp://mse.univ-paris1.fr/pub/mse/CES2010/10067.pdf
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Bibliographic Info

Paper provided by Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne in its series Documents de travail du Centre d'Economie de la Sorbonne with number 10067.

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Length: 34 pages
Date of creation: Jul 2010
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Handle: RePEc:mse:cesdoc:10067

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Keywords: Maximum likelihood method; related-GARCH process; recursive estimation method; mixture of Gaussian distributions; generalized hyperbolic distributions; SP500.;

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Cited by:
  1. Dominique Guegan & Florian Ielpo & Hanjarivo Lalaharison, 2011. "Option pricing with discrete time jump processes," Documents de travail du Centre d'Economie de la Sorbonne 11037, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.

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