Risk evaluation is a forecast, and its validity must be backtested. Probability distribution forecasts are used in this work and allow for more powerful validations compared to point forecasts. Our aim is to use bivariate copulas in order to characterize the in-sample copulas and to validate...

Consider a random sample from a continuous multivariate distribution function F with copula C. In order to test the null hypothesis that C belongs to a certain parametric family, we construct an under H0 asymptotically distribution-free process that serves as a tests generator. The process is a...

We propose a new approach to evaluating copula-based multivariate density forecasts. Employing Hansen’s SPA test and conducting multiple comparisons of fully-parametric models, our approach accommodates possible misspecifications in the multivariate joint and the univariate marginal...