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Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation

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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File URL: http://www.econ.canterbury.ac.nz/RePEc/cbt/econwp/1123.pdf
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Paper provided by University of Canterbury, Department of Economics and Finance in its series Working Papers in Economics with number 11/23.

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Length: 41 pages
Date of creation: 01 May 2011
Date of revision:
Handle: RePEc:cbt:econwp:11/23
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