Testing for structural breaks in correlation
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Date
2013-05-22
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Does it improve Value-at-Risk forecasting?
Abstract
In this paper, we compare the Constant Conditional Correlation (CCC) model to its dynamic
counterpart, the Dynamic Conditional Correlation (DCC) model with respect to its accuracy for forecasting
the Value-at-Risk of financial portfolios. Additionally, we modify these benchmark models by combining
them with a pairwise test for constant correlations, a test for a constant correlation matrix, and a test for a
constant covariance matrix. In an empirical horse race of these models based on five- and ten-dimensional
portfolios, our study shows that the plain CCC- and DCC-GARCH models are outperformed in several
settings by the approaches modified by tests for structural breaks in asset correlations and covariances.
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Keywords
CCC-GARCH, DCC-GARCH, estimation window, structural breaks, VaR-forecast