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Robust Ranking of Multivariate GARCH Models by Problem Dimension

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Caporin, Massimiliano and McAleer, Michael (2012) Robust Ranking of Multivariate GARCH Models by Problem Dimension. [ Documentos de Trabajo del Instituto Complutense de Análisis Económico (ICAE); nº 06, 2012, ] (Unpublished)

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Official URL: http://eprints.ucm.es/14821/


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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.


Item Type:Working Paper or Technical Report
Uncontrolled Keywords:Covariance forecasting, Model confidence set, Robust model ranking, MGARCH, Robust model comparison.
Subjects:Social sciences > Economics > Econometrics
Series Name:Documentos de Trabajo del Instituto Complutense de Análisis Económico (ICAE)
Volume:2012
Number:06
ID Code:14821
Deposited On:16 Apr 2012 11:23
Last Modified:06 Feb 2014 10:10

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