Robust Ranking of Multivariate GARCH Models by Problem Dimension

Michael McAleer and Massimiliano Caporin

No 815, KIER Working Papers from Kyoto University, Institute of Economic Research

Abstract: During 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. We provide an empirical comparison of alternative MGARCH models, namely BEKK, DCC, Corrected DCC (cDCC), CCC, OGARCH Exponentially Weighted Moving Average, and covariance shrinking, using historical data for 89 US equities. We contribute to the literature in several directions. First, we consider a wide range of models, including the recent cDCC and covariance shrinking models. Second, we use a range of tests and approaches for direct and indirect model comparison, including the Model Confidence Set. Third, we examine how the robust model rankings are influenced by the cross- sectional dimension of the problem.

Keywords: Covariance forecasting; model confidence set; robust model ranking; MGARCH; robust model comparison. (search for similar items in EconPapers)
Date: 2012-04
New Economics Papers: this item is included in nep-for
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Citations: View citations in EconPapers (10)

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Related works:
Journal Article: Robust ranking of multivariate GARCH models by problem dimension (2014)
Working Paper: Robust Ranking of Multivariate GARCH Models by Problem Dimension (2012)
Working Paper: Robust Ranking of Multivariate GARCH Models by Problem Dimension (2012)
Working Paper: Robust Ranking of Multivariate GARCH Models by Problem Dimension (2012)
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