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Bayesian Hierarchical Copula Models with a Dirichlet–Laplace Prior

Author

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  • Paolo Onorati

    (The Department MEMOTEF, Sapienza University of Rome, 00161 Roma, Italy
    These authors contributed equally to this work.)

  • Brunero Liseo

    (The Department MEMOTEF, Sapienza University of Rome, 00161 Roma, Italy
    These authors contributed equally to this work.)

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.

Suggested Citation

  • Paolo Onorati & Brunero Liseo, 2022. "Bayesian Hierarchical Copula Models with a Dirichlet–Laplace Prior," Stats, MDPI, vol. 5(4), pages 1-17, November.
    • Handle: RePEc:gam:jstats:v:5:y:2022:i:4:p:63-1078:d:960084
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    References listed on IDEAS

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