  We propose a novel Dirichlet-based P\'olya tree (D-P tree) prior on the
copula and based on the D-P tree prior, a nonparametric Bayesian inference
procedure. Through theoretical analysis and simulations, we are able to show
that the flexibility of the D-P tree prior ensures its consistency in copula
estimation, thus able to detect more subtle and complex copula structures than
earlier nonparametric Bayesian models, such as a Gaussian copula mixture.
Further, the continuity of the imposed D-P tree prior leads to a more favorable
smoothing effect in copula estimation over classic frequentist methods,
especially with small sets of observations. We also apply our method to the
copula prediction between the S\&P 500 index and the IBM stock prices during
the 2007-08 financial crisis, finding that D-P tree-based methods enjoy strong
robustness and flexibility over classic methods under such irregular market
behaviors.
