Testing for Structural Breaks in Correlations: Does it Improve Value-at-Risk Forecasting?

Abstract

In this paper, we modify the Constant Conditional Correlation (CCC) model and its dynamic counterpart, the Dynamic Conditional Correlation (DCC) model 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. We compare these models to their plain counterparts with respect to the accuracy for forecasting the Value-at-Risk of financial portfolios by a set of distinct backtests. In an empirical horse race of these models based on multivariate portfolios, our study shows that correlation models can be improved by approaches modified by tests for structural breaks in co-movements in several settings.