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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