  Regular vine copulas can describe a wider array of dependency patterns than
the multivariate Gaussian copula or the multivariate Student's t copula. This
paper presents two contributions related to model selection of regular vine
copulas. First, our pair copula family selection procedure extends existing
Bayesian family selection methods by allowing pair families to be chosen from
an arbitrary set of candidate families. Second, our method represents the first
Bayesian model selection approach to include the regular vine density
construction in its scope of inference. The merits of our approach are
established in a simulation study that benchmarks against methods suggested in
current literature. A real data example about forecasting of portfolio asset
returns for risk measurement and investment allocation illustrates the
viability and relevance of the proposed scheme.
