.png){kind=icon}
.png){kind=icon}
Return
Cluster GARCH
.png){kind=icon}
PRE
AI
DOI：10.1080/07350015.2025.2510325.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We introduce a novel multivariate GARCH model with flexible convolution-tdistributions that is applicable in high-dimensional systems. The model is calledCluster GARCHbecause 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-tdistribution provides a better empirical performance than the conventional multivariatet-distribution.
Keywords:
Block correlation matrix
Cluster structure
Heavy-tailed distributions
Multivariate GARCH
Score-driven model
Journal
J
IF:
2.5
Papers:
96
Citations:
9.1K
View more organizations