Bayesian clustering of many GARCH models

Luc Bauwens and Jeroen Rombouts

No 2003087, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: We consider the estimation of a large number of GARCH models, of the order of several hundreds. To achieve parsimony, we classify the series in a small number of groups. Within a cluster, the series share the same model and the same parameters. Each cluster contains therefore similar series. We do not know a priori which series belongs to which cluster. The model is a finite mixture of distributions, where the component weights are unknown parameters and each component distribution has its own conditional mean and variance. Inference is done by the Bayesian approach, using data augmentation techniques. Illustrations are provided.

Keywords: Bayesian inference; clustering; GARCH; Gibbs sampling; mixtures (search for similar items in EconPapers)
JEL-codes: C11 C32 (search for similar items in EconPapers)
Date: 2003-12
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Bayesian Clustering of Many Garch Models (2007)
Working Paper: Bayesian clustering of many GARCH models (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvco:2003087

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