发表期刊：Journal of Business & Economic Statistics
发表时间：July 2025
作者及单位：Chen Tong（Department of Finance, School of Economics, Xiamen University, Fujian, China; Wang Yanan Institute for Studies in Economics, Xiamen University）, Peter Reinhard Hansen*，llya Archakov
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 tail dependencies. The expressions for the log-likelihood function and its derivatives are tractable, and the latter facilitate a score-driven model for the dynamic correlation structure. We apply the Cluster GARCH model to daily returns for 100 assets and find that 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.
关键词：Multivariate GARCH；Score-Driven Model；Cluster Structure；Block Correlation Matrix；Heavy-Tailed Distributions
