Bayesian Clustering of Many Garch Models

Luc Bauwens and Jeroen Rombouts

Econometric Reviews, 2007, vol. 26, issue 2-4, 365-386

Abstract: We consider the estimation of a large number of GARCH models, of the order of several hundreds. Our interest lies in the identification of common structures in the volatility dynamics of the univariate time series. To do so, we classify the series in an unknown number of clusters. 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. Simulations and an illustration using data on U.S. stocks are provided.

Keywords: Bayesian inference; Clustering; GARCH; Gibbs sampling; Mixtures (search for similar items in EconPapers)
Date: 2007
References: View complete reference list from CitEc
Citations: View citations in EconPapers (20)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/07474930701220576 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Bayesian clustering of many GARCH models (2007)
Working Paper: Bayesian clustering of many GARCH models (2003)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:emetrv:v:26:y:2007:i:2-4:p:365-386

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/LECR20

DOI: 10.1080/07474930701220576

Access Statistics for this article

Econometric Reviews is currently edited by Dr. Essie Maasoumi

More articles in Econometric Reviews from Taylor & Francis Journals
Bibliographic data for series maintained by ().