Bayesian Nonparametric Inference for a Multivariate Copula Function

The paper presents a general Bayesian nonparametric approach

for estimating a high dimensional copula. We first introduce the skew-normal

copula, which we then extend to an infinite mixture model. The skew-normal

copula fixes some limitations in the Gaussian copula. An MCMC algorithm

is developed to draw samples from the correct posterior distribution and the

model is investigated using both simulated and real applications. Consistency

of the Bayesian nonparametric model is established.