Evaluating conditional covariance estimates via a new targeting approach and a networks-based analysis

TL;DR

This paper introduces a novel targeting approach for estimating conditional covariance matrices in financial markets, utilizing network analysis to evaluate asset relationships and improve model accuracy.

Contribution

It proposes a new targeting method for BEKK and DCC models based on stable asset correlations, enhancing covariance estimation in financial time series.

Findings

Effective in simulated data using known loss functions

Network analysis reveals asset groupings and model performance

Encouraging empirical results for small asset sets

Abstract

Modeling and forecasting of dynamically varying covariances have received much attention in the literature. The two most widely used conditional covariances and correlations models are BEKK and DCC. In this paper, we advance a new method to introduce targeting in both models to estimate matrices associated with financial time series. Our approach is based on specific groups of highly correlated assets in a financial market, and these relationships remain unaltered over time. 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 and Network analysis. We find 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.