Vine copula, constructed from bivariate copulas, provides great flexibility in modelling complex high-dimensional dependence. When applied to multi-population mortality modelling, vine copula yields significant improvement over traditional multivariate copulas. In this paper, we propose capture time-varying features dependence with dynamic regular (R-vine) which is built copulas parameters. We ...

Looking at bivariate copulas from the perspective of conditional distributions and considering weak convergence almost all yields notion convergence. At first glance, this for might seem far too restrictive to be any practical importance – in fact, given samples a copula C corresponding empirical do not converge weakly with probability one general. Within class Archimedean Extreme Value copulas...

Copulas can be used to describe multivariate dependence structures. We explore the rôle of copulas with fractal support in the study of association measures. 1 General introduction and motivation Copulas are of interest because they link joint distributions to their marginal distributions. Sklar [12] showed that, for any real-valued random variables X1 and X2 with joint distribution H, there ex...

The use of copula analysis to estimate the lifetime of bonds is gaining popularity in the financial sector. Taking this concept a step further, the following research project explores the use of the copula method in estimating the joint occurrence of bond default as a method of predictive analysis of bond lifetime. This is accomplished by means of an indepth discussion on copulas followed by a ...

A copula is best described, as in Joe (1997), as a multivariate distribution function that is used to bind each marginal distribution function to form the joint. The copula parameterises the dependence between the margins, while the parameters of each marginal distribution function can be estimated separately. This is a brief introduction to copulas and multivariate dependence issues within a h...

One of the main issues of risk management is the aggregation of individual risks. A powerful concept to aggregate the risks — the copula function — has been introduced in ...nance by Embrechts, McNeil, and Straumann [1999,2000]. In their papers, the authors clarify the essential concepts of dependence and correlation and certainly will greatly in‡uence the risk management industry. The goal of ...

One of the main issues of risk management is the aggregation of individual risks. A powerful concept to aggregate the risks — the copula function — has been introduced in finance by Embrechts, McNeil, and Straumann [1999,2000]. In their papers, the authors clarify the essential concepts of dependence and correlation and certainly will greatly influence the risk management industry. The goal of ...

Tail dependence and conditional tail dependence functions describe, respectively, the tail probabilities and conditional tail probabilities of a copula at various relative scales. The properties as well as the interplay of these two functions are established based upon their homogeneous structures. The extremal dependence of a copula, as described by its extreme value copulas, is shown to be co...