Cluster GARCH

TL;DR

The paper introduces Cluster GARCH, a flexible multivariate model that captures cluster structures in correlations and tail dependencies, demonstrating superior performance on high-dimensional financial data.

Contribution

It presents a novel multivariate GARCH model with convolution-t distributions that effectively models high-dimensional systems with cluster structures.

Findings

Outperforms existing models in-sample and out-of-sample.

Convolution-t distribution yields better empirical results than multivariate t.

Applicable to high-dimensional financial data with complex dependency structures.

Abstract

We introduce a novel multivariate GARCH model with flexible convolution-t distributions that is applicable in high-dimensional systems. The model is called Cluster GARCH because it can accommodate cluster structures in the conditional correlation matrix and in the tail dependencies. The expressions for the log-likelihood function and its derivatives are tractable, and the latter facilitate a score-drive model for the dynamic correlation structure. We apply the Cluster GARCH model to daily returns for 100 assets and find it outperforms existing models, both in-sample and out-of-sample. Moreover, the convolution-t distribution provides a better empirical performance than the conventional multivariate t-distribution.