Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation

Massimiliano Caporin and Michael McAleer

No 2011-20, Documentos de Trabajo del ICAE from Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico

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)
Pages: 40 pages
Date: 2011
New Economics Papers: this item is included in nep-ets and nep-for
Note: The authors wish to thank the Editor, Associate Editor, two referees, Christian Hafner, Sébastien Laurent, Francesco Violante, Roberto Casarin, Tommaso Proietti, Gian Piero Aielli, Adelchi Azzalini, Riccardo Jack Lucchetti, Eduardo Rossi, Giovanni Urga, participants at seminars in Louvain-la-Neuve and Zurich, and participants at the Italian Statistical Society XLV Conference, Padova, June 2010, CFE10 conference, London, December 2010, and ICEEE conference, Pisa, January 2011, for helpful comments and suggestions. For financial support, the second author wishes to thank the Australian Research Council, National Science Council, Taiwan, and the Japan Society for the Promotion of Science.
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https://eprints.ucm.es/id/eprint/12816/1/1120.pdf First draft: January 2009, This version: May 2011 (application/pdf)

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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