Bayesian Inference for Multivariate Copulas Using Pair-Copula Constructions
We provide a Bayesian analysis of pair-copula constructions (PCCs) (Aas et al.,�2009), which outperform many other multivariate copula constructions in modeling dependencies in financial data. We use bivariate t-copulas as building blocks in a PCC to allow extreme events in bivariate margins individually. While parameters may be estimated by maximum likelihood, confidence intervals are difficult to obtain. Consequently, we develop a Markov chain Monte Carlo (MCMC) algorithm and compute credible intervals. Standard errors obtained from MCMC output are compared to those obtained from a numerical Hessian matrix and bootstrapping. As applications, we consider Norwegian financial returns and Euro swap rates. Finally, we apply the Bayesian model selection approach of Congdon�(2006) to identify conditional independence, thus constructing more parsimonious PCCs. Copyright The Author 2010. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: firstname.lastname@example.org, Oxford University Press.
Volume (Year): 8 (2010)
Issue (Month): 4 (Fall)
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