Marginals Versus Copulas: Which Account For More Model Risk In Multivariate Risk Forecasting?

Multivariate probabilistic forecasting using Bayesian model averaging and copulas

We propose a method for post-processing an ensemble of multivariate forecasts in order to obtain a joint predictive distribution of weather. Our method utilizes existing univariate postprocessing techniques, in this case ensemble Bayesian model averaging (BMA), to obtain estimated marginal distributions. However, implementing these methods individually offers no information regarding the joint ...

Multivariate Operational Risk: Dependence Modelling with Lévy Copulas

Simultaneous modelling of operational risks occurring in different event type/business line cells poses the challenge for operational risk quantification. Invoking the new concept of Lévy copulas for dependence modelling yields simple approximations of high quality for multivariate operational VAR.

Multivariate Fréchet copulas and conditional value-at-risk

Based on the method of copulas, we construct a parametric family of multivariate distributions using mixtures of independent conditional distributions. The new family of multivariate copulas is a convex combination of products of independent and comonotone subcopulas. It fulfills the four most desirable properties that a multivariate statistical model should satisfy. In particular, the bivariat...

Fuzzy model for risk analysis

The goal of this paper is to show how the concept of fuzzy logic can be used to establish a degree to which an investment project belongs to a class of risk. Also, the probability of the fuzzy event is presented and is ap-plied to calculate the probability of the fuzzy event “the project X is a good investment”. This process has to enable the decision maker to compare several alternative invest...

A Multivariate Analysis for Risk Capital Estimation in Insurance Industry: Vine Copulas