Bayesian model choice of grouped t-copula

Xiaolin Luo*, Pavel V. Shevchenko

*Corresponding author for this work

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

One of the most popular copulas for modeling dependence structures is t-copula. Recently the grouped t-copula was generalized to allow each group to have one member only, so that a priori grouping is not required and the dependence modeling is more flexible. This paper describes a Markov chain Monte Carlo (MCMC) method under the Bayesian inference framework for estimating and choosing t-copula models. Using historical data of foreign exchange (FX) rates as a case study, we found that Bayesian model choice criteria overwhelmingly favor the generalized t-copula. In addition, all the criteria also agree on the second most likely model and these inferences are all consistent with classical likelihood ratio tests. Finally, we demonstrate the impact of model choice on the conditional Value-at-Risk for portfolios of six major FX rates.

Original languageEnglish
Pages (from-to)1097-1119
Number of pages23
JournalMethodology and Computing in Applied Probability
Volume14
Issue number4
DOIs
Publication statusPublished - 2012
Externally publishedYes

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