This paper introduces a copula based multivariate rank test for independence extending existing approaches from literature to p dimensions. Then, a multiparametric p-dimensional generalization of the FGM copula is provided that can model the behavior in each vertex of the p-dimensional unit cube...

In this paper, we study the asymptotic behavior of the sequential empirical process and the sequential empirical copula process, both constructed from residuals of multivariate stochastic volatility models. Applications for the detection of structural changes and specification tests of the...

The key to multiple testing is to respect the dependencies between the marginal hypotheses tests. Any dependency structure can be modeled by so-called copula functions. This makes copulas an interesting tool in multiple testing. In particular, it is possible to explicitly utilize the dependency...

A bivariate normal distribution, with the attendant non-analytically integrable p.d.f., lies at the hearts of many financial theories. Its derived Gaussian copula ostensibly does away with the normality assumptions, only to retain the linear (Pearson's) correlation measure implicit to said...

A novel, general two-sample hypothesis testing procedure is established for testing the equality of tail copulas associated with bivariate data. More precisely, using an ingenious transformation of a natural two-sample tail copula process, a test process is constructed, which is shown to...