Smooth Nonparametric Bernstein Vine Copulas

Abstract

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.