Cluster GARCH
arxiv(2024)
摘要
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.
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