Archimedean copulae build a large family of copulae exhibiting tail-dependency in many cases. We extend the classical homogeneous (exchangeable) Archimedean copula to the heterogeneous case. This will extend the use of this copula family to multivariate random variable with pairwise different...

Copulas have become an important tool in the modern best practice Enterprise Risk Management, often supplanting other approaches to modelling stochastic dependence. However, choosing the 'right' copula is not an easy task, and the temptation to prefer a tractable rather than a meaningful...

We study symmetry properties of bivariate copulas. For this, we introduce an order of asymmetry, as well as measures of asymmetry which are monotone in that order. In an empirical study, we illustrate that asymmetric dependence structures do indeed occur in financial market data and discuss its...

Understanding the time series dynamics of a multivariate dimensional dependency structure is a challenging task. A multivariate covariance driven Gaussian or mixed normal time varying models are limited in capturing important data features such as heavy tails, asymmetry, and nonlinear...

We propose a multidimensional extension for Patton's (2006) bivariate Dynamic Copulas. We also introduce a Dynamic Mixture Copula whose parameters and weights follow well defined dynamic processes. Both approaches are more flexible to adapt to financial data than currently available Copula...

When dealing with multi-issuer credit derivatives such as CDO, it is customary to refer the reader to either of two approaches: “static models” which focus on the copula between the variables of interest, and “dynamic models” where the diffusion of the underlying variables is described...