Tails of Archimedean copulas

Publication de Tails of Archimedean Copulas, coécrit avec Johan Segers, dans le Journal of Multivariate Analysis, enfin… en ligne sur http://sciencedirect.com/science…

A complete and user-friendly directory of tails of Archimedean copulas is presented which can be used in the selection and construction of appropriate models with desired properties. The results are synthesized in the form of a decision tree: Given the values of some readily computable characteristics of the Archimedean generator, the upper and lower tails of the copula are classified into one of three classes each, one corresponding to asymptotic dependence and the other two to asymptotic independence. For a long list of single-parameter families, the relevant tail quantities are computed so that the corresponding classes in the decision tree can easily be determined. In addition, new models with tailor-made upper and lower tails can be constructed via a number of transformation methods. The frequently occurring category of asymptotic independence turns out to conceal a surprisingly rich variety of tail dependence structures.

Un peu d’autocélébration…

Le dernier numéro l’Argus de l’Assurance parle de moi dans la dernière page ! c’est le début de la gloire…comment ça je prends la grosse tête ? Deux semaines après François de Varenne… Plus sérieusement, j’espère que cela puisse faire parler un peu de la formation proposée à Rennes.

Je reviendrais à la rentrée sur certains points (c’est mon côté “insolent”) pour compléter… Sinon le “diablo rojo” mentionné n’est plus ici, la vidéo est en ligne sur http://youtube.com/…

Et l’article peut se lire ci-dessous,

Estimation de quantile par noyau beta

Le papier sur l’estimation de quantile par noyau beta, coécrit avec Abder Oulidi, est accepté pour publication dans Statistics and Computing, http://link.springer.com/…

In this paper we propose several nonparametric estimators of quantiles based on Beta kernel and applied to transformed data by the generalized Champernowne distribution initially fitted to the data. A Monte-Carlo based study will show that those estimators improve the efficiency of a traditional ones, not only for light tailed distributions, but also heavy tails, when the probability level is close to 1. We also compare these estimators with the Extreme Value Theory Quantile applying to Danish data on large fire insurance losses.