The purpose of this paper is to present a comprehensive simulation study on the finite sample properties of minimum-distance and maximum-likelihood estimators for bivariate and multivariate parametric copulas. For five popular parametric copulas, classical maximum-likelihood is compared to a...

We propose the use of 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...

We propose to model the joint distribution of bid-ask spreads and log returns of a stock portfolio by using Autoregressive Conditional Double Poisson and GARCH processes for the marginals and vine copulas for the dependence structure. By estimating the joint multivariate distribution of both...

In this paper, the optimality of bivariate copula-VaR models and the usefulness of several goodness-of-fit tests for copulas are analysed in a comprehensive empirical study using data for stocks, commodities and FX futures. In particular, I try to answer two questions: (1) Which parametric...

The purpose of this paper is to present a comprehensive Monte Carlo simulation study on the performance of minimum-distance (MD) and maximum-likelihood (ML) estimators for bivariate parametric copulae. In particular, I consider Cramer-von-Mises-, Kolmogorov-Smirnov- and L1-variants of the...

We study symmetry properties of bivariate copulas. For this, we introduce an order of asymmetry, as well as measures of asymmetry which are monotone in that order. In an empirical study, we illustrate that asymmetric dependence structures do indeed occur in financial market data and discuss its...

We introduce a new stochastic order for the tail dependence between random variables. We then study different measures of tail dependence which are monotone in the proposed order, thereby extending various known tail dependence coefficients from the literature. We apply our concepts in an...