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Variance Clustering Improved Dynamic Conditional Correlation MGARCH Estimators

  • Gian Piero Aielli

    ()

    (University of Padova)

  • Massimiliano Caporin

    ()

    (University of Padova)

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.

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Paper provided by Dipartimento di Scienze Economiche "Marco Fanno" in its series "Marco Fanno" Working Papers with number 0133.

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Length: 56 pages
Date of creation: May 2011
Date of revision:
Handle: RePEc:pad:wpaper:0133
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