Bayesian Hierarchical Copula Models with a Dirichlet-Laplace Prior
Methodology
2025-02-07 v2
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.
Keywords
Cite
@article{arxiv.2202.13689,
title = {Bayesian Hierarchical Copula Models with a Dirichlet-Laplace Prior},
author = {Paolo Onorati and Brunero Liseo},
journal= {arXiv preprint arXiv:2202.13689},
year = {2025}
}