Dynamic Conditional Correlation Models in a Multiple Financial Asset Portfolio

Abstract

A key concept in risk and portfolio management is diversification, which has recently come under intense scrutiny following dramatic market movements where historical correlation patterns have collapsed. To counter the devastating effects from these coordinated sell-off events, academics have developed several models to forecast correlation. This article examines two recent dynamic conditional correlation models: an asymmetric dynamic conditional correlation (ADCC) model and a dynamic equicorrelation (DECO) model. Previous research has demonstrated the merits of these advanced models in an artificial setting, but a study in a high-dimensional, though still artificial, setting was inconclusive on the merits. This article casts doubt on the merits of these models in a real-world setting. The results show that neither the ADCC model nor the DECO model provides any additional information over a sample-based approach when evaluated using a simple parametric covariance-variance value at risk (VaR) measurement. Used in risk monitoring, the models provide sound risk estimates but no additional insights into forecasting correlation spikes.