Likelihood-Related Estimation Methods and Non-Gaussian GARCH Processes

Christophe Chorro (), Dominique Guegan () and Florian Ielpo
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Christophe Chorro: Centre d'Economie de la Sorbonne - Paris School of Economics, https://centredeconomiesorbonne.cnrs.fr
Dominique Guegan: Centre d'Economie de la Sorbonne - Paris School of Economics, https://cv.archives-ouvertes.fr/dominique-guegan

Documents de travail du Centre d'Economie de la Sorbonne from Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne

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

Keywords: Maximum likelihood method; related-GARCH process; recursive estimation method; mixture of Gaussian distributions; generalized hyperbolic distributions; SP500 (search for similar items in EconPapers)
JEL-codes: C22 G13 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2010-07
New Economics Papers: this item is included in nep-ets and nep-ore
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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http://mse.univ-paris1.fr/pub/mse/CES2010/10067.pdf (application/pdf)

Related works:
Working Paper: Likelihood-Related Estimation Methods and Non-Gaussian GARCH Processes (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:10067

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