Erhardt and Czado (2008) suggest an approximative method for sampling highdimensional count random variables with a specified Pearson correlation. They utilize Gaussian copulae for the construction of multivariate discrete distributions. A major task is to determine the appropriate copula parameters for the achievement of a specified target correlation. Erhardt and Czado (2008) develop an optim...

We examine the standard Gaussian copula model for correlated defaults (also called the survival copula) and its relationship with the theoretically richer model based on diffusion processes and default thresholds. We show that in a discrete time framework the Gaussian copula can be seen as a simple global approximation to the Brownian copula implied by correlated diffusions. More precisely, the...

The authors extend to multivariate contexts the copula-based univariate time series modeling approach of Chen & Fan [X. Chen, Y. Fan, Estimation of copula-based semiparametric time series models, J. Econometrics 130 (2006) 307–335; X. Chen, Y. Fan, Estimation and model selection of semiparametric copula-based multivariate dynamic models under copula misspecification, J. Econometrics 135 (2006) ...

For a multivariate vector X with discrete components, we construct, by means of explicit randomised transformations of X, multivariate couplings of copula representers U associated to X. As a result, we show that any copula can be constructed in this manner, giving a full probabilistic characterisation of the set of copula functions associated to X. The dependence properties of these copula rep...

One of the biggest advances in recent years for high-dimensional copula models and applications has been the development of the vine pair-copula construction that covers continuous and discrete variables, and its extensions to include latent variables. Software has been made available in the VineCopula R package and the package that is companion to the book by Joe [6]. This special issue of the...

We propose a comprehensive Bayesian approach for graphical model determination in observational studies that can accommodate binary, ordinal or continuous variables simultaneously. Our new models are called copula Gaussian graphical models and embed graphical model selection inside a semiparametric Gaussian copula. The domain of applicability of our methods is very broad and encompass many stud...

We clarify the link between the notion of singular copula and the concept of support of the measure induced by a copula.

to analyze and manage the risk of drought in a region, access to some certain information is required, the most important of which is the frequency of drought severity-duration. realizing the high correlation of these factors, one must pick up a method that pinpoint the relation and effects of these factors on drought analysis. in this paper, numerous copula families were employed to model the ...

Copulas are used to depict dependence among several random variables. Both parametric and non-parametric estimation methods have been studied in the literature. Moreover, profile empirical likelihood methods based on either empirical copula estimation or smoothed copula estimation have been proposed to construct confidence intervals of a copula. In this paper, a jackknife empirical likelihood m...