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BL-GARCH model with elliptical distributed innovations

  • Abdou Kâ Diongue

    ()

    (UFR SAT - Université Gaston Berger de Saint-Louis Sénégal - Université Gaston Berger de Saint-Louis)

  • Dominique Guegan

    ()

    (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris I - Panthéon-Sorbonne, EEP-PSE - Ecole d'Économie de Paris - Paris School of Economics - Ecole d'Économie de Paris)

  • Rodney C. Wolff

    ()

    (School of Mathematical Sciences - Queensland University of Technology)

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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Paper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number halshs-00368340.

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Date of creation: Jul 2010
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Publication status: Published, Journal of Statistical Computation and Simulation, 2010, 80, 7, 775-791
Handle: RePEc:hal:cesptp:halshs-00368340
Note: View the original document on HAL open archive server: http://halshs.archives-ouvertes.fr/halshs-00368340
Contact details of provider: Web page: http://hal.archives-ouvertes.fr/

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