Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/70636
Authors: 
Jensen, Mark J.
Maheu, John M.
Year of Publication: 
2012
Series/Report no.: 
Working Paper, Federal Reserve Bank of Atlanta 2012-9
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.
Subjects: 
Bayesian nonparametrics
cumulative Bayes factor
Dirichlet process mixture
forecasting
infinite mixture model
MCMC
slice sampler
JEL: 
C11
C14
C53
C58
Document Type: 
Working Paper

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