Bayesian Clustering of Many GARCH Models
CORE Discussion Paper No. 2003/87
42 Pages Posted: 14 Apr 2005
Date Written: November 28, 2003
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
JEL Classification: C11, C32
Suggested Citation: Suggested Citation