Dynamic Conditional Correlations with Partial Information Pooling

We propose a novel Dynamic Conditional Correlation model with Conditional Linear Information Pooling (CLIP-DCC) which endogenously determines an optimal degree of commonality in the correlation innovations. Effectively, this allows a part of the update of each individual correlation to parsimoniously depend on the information contained in all asset return pairs. In contrast to existing approaches, such as the Dynamic EquiCOrrelation (DECO) model, the CLIP-DCC model does not restrict long-run behavior, thereby naturally complementing target correlation matrix shrinkage approaches. Empirical findings suggest substantial benefits for a minimum-variance investor in real-time. Combining the CLIP-DCC model with target shrinkage yields additive improvements, confirming that they address distinct parts of uncertainty of the conditional correlation matrix.