Conditional tail independence in Archimedean copula models

Nonparametric estimation of the conditional tail copula

The tail copula is widely used to describe the dependence in the tail of multivariate distributions. In some situations such as risk management, the dependence structure may be linked with some covariate. The tail copula thus depends on this covariate and is referred to as the conditional tail copula. The aim of this paper is to propose a nonparametric estimator of the conditional tail copula a...

Comments on: Inference in multivariate Archimedean copula models

Goodness-of-fit Tests for Archimedean Copula Models

In this paper, we propose two tests for parametric models belonging to the Archimedean copula family, one for uncensored bivariate data and the other one for right-censored bivariate data. Our test procedures are based on the Fisher transform of the correlation coefficient of a bivariate (U, V ), which is a one-toone transform of the original random pair (T1, T2) that can be modeled by an Archi...

Conditional Independence Models via Filtrations

We formulate a novel approach to infer conditional independence models or Markov structure of a multivariate distribution. Specifically, our objective is to place informative prior distributions over decomposable graphs and sample efficiently from the induced posterior distribution. The key idea we develop in this paper is a parametrization of decomposable hypergraphs using the geometry of poin...

Dependence and Conditional Independence in Economic Models ∗

De Finetti’s Theorem asserts that a sequence of exchangeable variables are conditionally independent, that is, dependence under exchangeability is nothing but conditionally independence. However, de Finetti’s theorem is valid only with a infinite number of random variables, which makes this result unsuitable for application in many economic models. In this paper, we prove that dependence is con...