Work

Bayesian Hierarchical Copula Models with a Dirichlet–Laplace Prior

Year: 2022

Type: article

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.

Source: Stats

DOI: 10.3390/stats5040063

Authors: Paolo Onorati, Brunero Liseo

Institution: Sapienza University of Rome

Language: English

Topic: Financial Risk and Volatility Modeling

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,256

Funder: Sapienza Università di Roma