Variance Clustering Improved Dynamic Conditional Correlation MGARCH Estimators

Gian Piero Aielli () and Massimiliano Caporin
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Gian Piero Aielli: University of Padova

No 133, "Marco Fanno" Working Papers from Dipartimento di Scienze Economiche "Marco Fanno"

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 cluster- ing 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 modelÕs 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. (search for similar items in EconPapers)
JEL-codes: C32 C38 C51 C52 C53 C58 (search for similar items in EconPapers)
Pages: 56 pages
Date: 2011-05
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (7)

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Journal Article: Variance clustering improved dynamic conditional correlation MGARCH estimators (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:pad:wpaper:0133

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