Pooling Dynamic Conditional Correlation Models

Abstract

The Dynamic Conditional Correlation (DCC) model by Engle (2002) has become an extremely popular tool for modeling the time-varying dependence of asset returns. However, applications to large cross-sections have been found to be problematic, due to the curse of dimensionality. We propose a novel DCC model with Conditional LInear Pooling (CLIP-DCC) which endogenously determines an optimal degree of commonality in the correlation innovations, allowing a part of the update to be of reduced dimension. 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 the largest improvements, confirming that they address distinct parts of uncertainty of the conditional correlation matrix.

Projected Dynamic Conditional Correlations

“…The conditional variances are modeled using GARCH whereas the conditional correlation matrix is modeled using the Dynamic Conditional Correlation (DCC) model. Recent research in the literature that is based on GARCH-DCC includes Brownlees and Engle (2017), De Nard, Ledoit, and Wolf (2021), Engle, Ledoit, and Wolf (2019) and Van Os and Van Dijk (2021).…”