Bayesian semiparametric multivariate GARCH modeling

Mark Jensen and John Maheu

Working Papers from University of Toronto, Department of Economics

Abstract: This paper proposes a Bayesian nonparametric modeling approach for the return distribution in multivariate GARCH models. In contrast to the parametric literature the return distribution can display general forms of asymmetry and thick tails. An infinite mixture of multivariate normals is given a flexible Dirichlet process prior. The GARCH functional form enters into each of the components of this mixture. We discuss conjugate methods that allow for scale mixtures and nonconjugate methods which provide mixing over both the location and scale of the normal components. MCMC methods are introduced for posterior simulation and computation of the predictive density. Bayes factors and density forecasts with comparisons to GARCH models with Student-t innovations demonstrate the gains from our flexible modeling approach.

Keywords: Dirichlet process mixture; slice sampling (search for similar items in EconPapers)
JEL-codes: C11 C14 C32 C53 C58 (search for similar items in EconPapers)
Pages: Unknown pages
Date: 2012-06-29
New Economics Papers: this item is included in nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Bayesian semiparametric multivariate GARCH modeling (2013)
Working Paper: Bayesian semiparametric multivariate GARCH modeling (2012)
Working Paper: Bayesian Semiparametric Multivariate GARCH Modeling (2012)
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