BL-GARCH model with elliptical distributed innovations

Abstract : In this paper, we discuss the class of Bilinear GATRCH (BL-GARCH) models which are capable of capturing simultaneously two key properties of non-linear time series : volatility clustering and leverage effects. It has been observed often that the marginal distributions of such time series have heavy tails ; thus we examine the BL-GARCH model in a general setting under some non-Normal distributions. We investigate some probabilistic properties of this model and we propose and implement a maximum likelihood estimation (MLE) methodology. To evaluate the small-sample performance of this method for the various models, a Monte Carlo study is conducted. Finally, within-sample estimation properties are studied using S&P 500 daily returns, when the features of interest manifest as volatility clustering and leverage effects.
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Contributeur : Dominique Guégan <>
Soumis le : mercredi 15 avril 2009 - 21:09:02
Dernière modification le : mardi 23 juillet 2019 - 11:36:03
Document(s) archivé(s) le : mardi 8 juin 2010 - 20:26:38


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Abdou Kâ Diongue, Dominique Guegan, Rodney C. Wolff. BL-GARCH model with elliptical distributed innovations. Journal of Statistical Computation and Simulation, Taylor & Francis, 2010, 80 (7), pp.775-791. ⟨10.1080/00949650902773577⟩. ⟨halshs-00368340⟩



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