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BL-GARCH models with elliptical distributed innovations

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Author Info
Abdou Kâ Diongue () (UFR SAT - Université Gaston Berger - Université Gaston Berger de Saint-Louis)
Dominique Guegan () (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I, 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)

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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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Paper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number halshs-00368340_v1.

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Date of creation: 2009
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Publication status: Published, Journal of Statistical Computation and Simulation, 2009, Online First
Handle: RePEc:hal:cesptp:halshs-00368340_v1

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Related research
Keywords: BL-GARCH process - elliptical distribution - leverage effects - Maximum Likelihood - Monte Carlo method - volatility clustering;

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This page was last updated on 2009-12-4.

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