Ranking Multivariate GARCH Models by Problem Dimension:An Empirical Evaluation

Michael McAleer and Massimiliano Caporin

No 778, KIER Working Papers from Kyoto University, Institute of Economic Research

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.

Keywords: Covariance forecasting; model confidence set; model ranking; MGARCH; model comparison. (search for similar items in EconPapers)
JEL-codes: C32 C52 C53 (search for similar items in EconPapers)
Date: 2011-06
References: Add references at CitEc
Citations: View citations in EconPapers (3)

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Related works:
Working Paper: Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation (2011)
Working Paper: Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation (2011)
Working Paper: Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation (2011)
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