Bayesian Hierarchical Copula Models with a Dirichlet-Laplace Prior

TL;DR

This paper introduces a Bayesian hierarchical copula model with a Dirichlet-Laplace prior for clustering financial time series, addressing prior distribution issues and capturing dependence structures.

Contribution

It proposes a proper global-local shrinkage prior for hierarchical copula models, improving posterior validity and dependence modeling in financial data analysis.

Findings

Model performs well in simulations

Effective in real data clustering

Captures dependence among clusters

Abstract

We discuss a Bayesian hierarchical copula model for clusters of financial time series. A similar approach has been developed in recent paper. However, the prior distributions proposed there do not always provide a proper posterior. In order to circumvent the problem, we adopt a proper global-local shrinkage prior, which is also able to account for potential dependence structures among different clusters. The performance of the proposed model is presented via simulations and a real data analysis.