Cluster GARCH
Econometrics
2024-06-12 v1 Risk Management
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.
Keywords
Cite
@article{arxiv.2406.06860,
title = {Cluster GARCH},
author = {Chen Tong and Peter Reinhard Hansen and Ilya Archakov},
journal= {arXiv preprint arXiv:2406.06860},
year = {2024}
}