Variance Clustering Improved Dynamic Conditional Correlation MGARCH Estimators

55 Pages Posted: 13 May 2011

See all articles by Gian Piero Aielli

Gian Piero Aielli

Independent

Massimiliano Caporin

University of Padua - Department of Statistical Sciences

Date Written: May 11, 2011

Abstract

It is well-known that the estimated GARCH dynamics exhibit common patterns. Starting from this fact we extend the Dynamic Conditional Correlation (DCC) model by allowing for a clustering structure of the univariate GARCH parameters. The model can be estimated in two steps, the first devoted to the clustering structure, and the second focusing on correlation parameters. Differently from the traditional two-step DCC estimation, we get large system feasibility of the joint estimation of the whole set of model's parameters. We also present a new approach to the clustering of GARCH processes, which embeds the asymptotic properties of the univariate quasi-maximum-likelihood GARCH estimators into a Gaussian mixture clustering algorithm. Unlike other GARCH clustering techniques, our method logically leads to the selection of the optimal number of clusters.

Keywords: dynamic conditional correlations, time series clustering, multivariate GARCH, composite likelihood

JEL Classification: C32, C38, C53, C51, C52, C58

Suggested Citation

Aielli, Gian Piero and Caporin, Massimiliano, Variance Clustering Improved Dynamic Conditional Correlation MGARCH Estimators (May 11, 2011). Available at SSRN: https://ssrn.com/abstract=1838182 or http://dx.doi.org/10.2139/ssrn.1838182

Gian Piero Aielli

Independent ( email )

Massimiliano Caporin (Contact Author)

University of Padua - Department of Statistical Sciences ( email )

Via Battisti, 241
Padova, 35121
Italy

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