A Network-Based Analysis for Evaluating Conditional Covariance Estimates

Abstract

The modeling and forecasting of dynamically varying covariances has received a great deal of attention in the literature. The two most widely used conditional covariances and correlations models are BEKK and the DCC. In this paper, we advance a new method based on network analysis and a new targeting approach for both the above models with the aim of better estimating covariance matrices associated with financial time series. Our approach is based on specific groups of highly correlated assets in a financial market and assuming that those relationships remain unaltered at least in the long run. Based on the estimated parameters, we evaluate our targeting method on simulated series by referring to two well-known loss functions introduced in the literature. Furthermore, we find and analyze all the maximal cliques in correlation graphs to evaluate the effectiveness of our method. Results from an empirical case study are encouraging, mainly when the number of assets is not large.