  We propose to use nonparametric Bernstein copulas as bivariate pair-copulas
in high-dimensional vine models. The resulting smooth and nonparametric vine
copulas completely obviate the error-prone need for choosing the pair-copulas
from parametric copula families. By means of a simulation study and an
empirical analysis of financial market data, we show that our proposed smooth
nonparametric vine copula model is superior to competing parametric vine models
calibrated via Akaike's Information Criterion.
