39 Pages Posted: 17 May 2013 Last revised: 16 Mar 2015
Date Written: March 11, 2015
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.
Keywords: CCC-GARCH, DCC-GARCH, Estimation window, Structural breaks, VaR-forecast
JEL Classification: C32, C41, C53, G17, G32
Suggested Citation: Suggested Citation
Berens, Tobias and Weiss, Gregor N. F. and Wied, Dominik, Testing for Structural Breaks in Correlations: Does it Improve Value-at-Risk Forecasting? (March 11, 2015). Journal of Empirical Finance, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2265488 or http://dx.doi.org/10.2139/ssrn.2265488