We introduce strong convergence in regard to approximation of copulas. This new type of convergence is useful in dealing with the-product of Darsow, Nguyen, and Olsen for copulas. We also provide tools for constructing strong approximations of copulas by using partitions of unity.

Assuming absolute continuity of marginals, we give the distribution for sums of dependent random variables from some class of Archimedean copulas and the marginal distribution functions of all order statistics.We use conditional independence structure of random variables from this class of Archimedean copulas and Laplace transform. Additionally, we present an application of our results to VaR e...

In Aas et al. (2009) and Aas and Berg (2009), it is shown that vine copulas constructed from bivariate t-copulas can provide better fits to multivariate financial asset return data. Several published articles indicate that for several assets there might be stronger tail dependence of returns in the joint lower tail than upper tail. We use vine copula models with appropriate choices of bivariate...

In this paper we analyse the properties of hierarchical Archimedean copulas. This class is a generalisation of the Archimedean copulas and allows for general non-exchangeable dependency structures. We show that the structure of the copula can be uniquely recovered from all bivariate margins. We derive the distribution of the copula value, which is particularly useful for tests and constructing ...

Institute of Mathematics of the Academy of Sciences of the Czech Republic provides access to digitized documents strictly for personal use. Each copy of any part of this document must contain these Terms of use. This paper has been digitized, optimized for electronic delivery and stamped with digital signature within the project DML-CZ: The Czech Digital Mathematics Library In the paper, binary...

Copulas are used to model multivariate data as they account for the dependence structure and provide a flexible representation of the multivariate distribution. A great number of copulas has been proposed with various dependence aspects. One important issue is the choice of an appropriate copula from a large set of candidate families to model the data at hand. A large number of copulas are comp...

Copulas [18] link univariate marginal distribution functions into a joint distribution function of the corresponding random vector. In this paper we will deal with bivariate copulas only. Recall that a function C : [0, 1] → [0, 1] is a (bivariate) copula whenever it is grounded, C(x, y) = 0 whenever 0 ∈ {x, y}, it has neutral element 1, C(x, y) = x∧y, whenever 1 ∈ {x, y} and it is 2-increasing,...

In this contribution we review models for construction of higher dimensional dependence that have arisen recent years. In particular we focus on specific generalized Farlie Gumbel (or Sarmanov) copulas which are generated by a single function (so-called generator or generator function) defined on the unit interval. An alternative approach to generalize the FGM family of copulas is to consider t...