The estimation of dependencies between multiple variables is a central problem in the analysis of financial time series. A common approach is to express these dependencies in terms of a copula function. Typically the copula function is assumed to be constant but this may be inaccurate when there are covariates that could have a large influence on the dependence structure of the data. To account...

The paper deals with modeling of mutual dependencies among financial assets. Its aim is to investigate the impact of different copula assumptions on optimal portfolios, when CVaR optimization is used. Strategic asset allocation perspective is supposed. It is demonstrated that copula functions enable us to separate the modeling of dependency features of financial assets from the modeling of marg...

In this paper, we seek to examine the effect of the presence of long memory on the dependence structure between financial returns and on portfolio optimization. First, we focus on the dependence structure using copulas. To select the best copula, in addition to the goodness of fit tests, we employ a graphical method based on visual comparison of the fitted copula density and the smoothed copula...

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.