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Ranking Multivariate GARCH Models by Problem Dimension:An Empirical Evaluation

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  • Michael McAleer

    (Erasmus University Rotterdam, Tinbergen Institute, The Netherlands, Complutense University of Madrid, and Institute of Economic Research, Kyoto University)

  • Massimiliano Caporin

    (Department of Economics and Management “Marco Fanno”University of Padova)

Abstract

In the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. Recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. In this paper, we provide an empirical comparison of a set of models, namely BEKK, DCC, Corrected DCC (cDCC) of Aeilli (2008), CCC, Exponentially Weighted Moving Average, and covariance shrinking, using historical data of 89 US equities. Our methods follow part of the approach described in Patton and Sheppard (2009), and the paper contributes to the literature in several directions. First, we consider a wide range of models, including the recent cDCC model and covariance shrinking. Second, we use a range of tests and approaches for direct and indirect model comparison, including the Weighted Likelihood Ratio test of Amisano and Giacomini (2007). Third, we examine how the model rankings are influenced by the cross-sectional dimension of the problem.

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Paper provided by Kyoto University, Institute of Economic Research in its series KIER Working Papers with number 778.

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Date of creation: Jun 2011
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Handle: RePEc:kyo:wpaper:778

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Keywords: Covariance forecasting; model confidence set; model ranking; MGARCH; model comparison.;

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