Nonparametric Bayesian modeling on infinite mixture Student t copulas

Abstract

This article introduces an infinite mixture Student t copula model using a nonparametric Bayesian approach. We establish a corresponding Markov chain Monte Carlo sampler for this model. In contrast to the normal mixture model, our proposed model is more suitable for data exhibiting tail dependence, which is frequently encountered in financial risk management. We evaluated the proposed algorithm through theoretical simulations and real data analysis. Parameter estimation results from the simulations demonstrate that our approach is competitive when compared to the standard maximum likelihood estimation method. The analysis of real financial data supports the validity of our approach and highlights the importance of applying a t copula in the presence of heavy tails.

Keywords:

• Copula
• Dependence modeling
• Infinite mixture model
• Markov chain Monte Carlo
• Nonparametric Bayesian

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Please contact the corresponding author should you require access to the dataset or $\mathsf{R}$ code associated with the proposed algorithm. The author will be happy to provide these materials upon request.