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Two-stage non Gaussian QML estimation of GARCH models and testing the efficiency of the Gaussian QMLE

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Author Info

  • Francq, Christian
  • Lepage, Guillaume
  • Zakoïan, Jean-Michel

Abstract

In generalized autoregressive conditional heteroskedastic (GARCH) models, the standard identifiability assumption that the variance of the iid process is equal to 1 can be replaced by an alternative moment assumption. We show that, for estimating the original specification based on the standard identifiability assumption, efficiency gains can be expected from using a quasi-maximum likelihood (QML) estimator based on a non Gaussian density and a reparameterization based on an alternative identifiability assumption. A test allowing to determine whether a reparameterization is needed, that is, whether the more efficient QMLE is obtained with a non Gaussian density, is proposed.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 165 (2011)
Issue (Month): 2 ()
Pages: 246-257

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Handle: RePEc:eee:econom:v:165:y:2011:i:2:p:246-257

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Web page: http://www.elsevier.com/locate/jeconom

Related research

Keywords: Conditional heteroskedasticity; Efficiency of estimators; Quasi maximum likelihood estimation;

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Cited by:
  1. Ke. Zhu, 2013. "A mixed portmanteau test for ARMA-GARCH models by the quasi-maximum exponential likelihood estimation approach," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(2), pages 230-237, 03.
  2. Christan Francq & Jean-Michel Zakoian, 2012. "Optimal Predictions of Powers of Conditionally Heteroskedastic Processes," Working Papers 2012-17, Centre de Recherche en Economie et Statistique.
  3. El Ghourabi, Mohamed & Francq, Christian & Telmoudi, Fedya, 2013. "Consistent estimation of the Value-at-Risk when the error distribution of the volatility model is misspecified," MPRA Paper 51150, University Library of Munich, Germany.
  4. Francq, Christian & Zakoian, Jean-Michel, 2012. "Risk-parameter estimation in volatility models," MPRA Paper 41713, University Library of Munich, Germany.

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