BAYESIAN CLUSTERING OF SIMILAR MULTIVARIATE GARCH MODELS

Luc Bauwens and Jeroen Rombouts

No 370, Econometric Society 2004 North American Winter Meetings from Econometric Society

Abstract: We consider the estimation of a large number of GARCH models, say of the order of several hundreds. Especially in the multivariate case, the number of parameters is extremely large. To reduce this number and render estimation feasible, we regroup the series in a small number of clusters. Within a cluster, the series share the same model and the same parameters. Each cluster should therefore contain similar series. What makes the problem interesting is that we do not know a piori which series belongs to which cluster. The overall model is therefore a finite mixture of distributions, where the weights of the components are unknown parameters and each component distribution has its own conditional mean and variance specification. Inference is done by the Bayesian approach, using data augmentation techniques. Illustrations are provided.

Keywords: Large financial systems; Multivariate GARCH; Clustering; Bayesian methods; Gibbs sampling; Finite mixture distributions (search for similar items in EconPapers)
JEL-codes: C11 C32 (search for similar items in EconPapers)
Date: 2004-08-11
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-fin
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Persistent link: https://EconPapers.repec.org/RePEc:ecm:nawm04:370

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