Split-Session Cluster GARCH for Overnight and Intraday Returns: The Role of Tail Heterogeneity

We propose the Split-Session Cluster GARCH model for heavy-tailed multivariate dependence among asset returns decomposed into overnight and intraday components.

The model uses convolution-$t$ distributions to allow tail behavior to differ across clusters defined by trading sessions and, within each session, by economic sectors.

It also accommodates block-structured conditional correlation matrices, preserving parsimony and scalability in high-dimensional settings.

The resulting likelihood remains tractable and yields a score-driven specification for dynamic correlations.

We apply the model to U.S. equity returns in six-asset and 100-asset applications.

The results reveal pronounced tail heterogeneity between overnight and intraday returns.

Model comparisons show that session-specific tail parameters substantially improve fit relative to a common multivariate-$t$ specification, while sector-level tail partitioning delivers additional gains concentrated mainly in the overnight component.

In the 100-asset application, asset-level tail heterogeneity delivers the strongest out-of-sample likelihood and global minimum-variance (GMV) portfolio performance.