On an asymmetric extension of multivariate Archimedean copulas based on quadratic form - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Dependence Modeling Année : 2016

On an asymmetric extension of multivariate Archimedean copulas based on quadratic form

Résumé

An important topic in Quantitative Risk Management concerns the modeling of dependence among risk sources and in this regard Archimedean copulas appear to be very useful. However, they exhibit symmetry, which is not always consistent with patterns observed in real world data. We investigate extensions of the Archimedean copula family that make it possible to deal with asymmetry. Our extension is based on the observation that when applied to the copula the inverse function of the generator of an Archimedean copula can be expressed as a linear form of generator inverses. We propose to add a distortion term to this linear part, which leads to asymmetric copulas. Parameters of this new class of copulas are grouped within a matrix, thus facilitating some usual applications as level curve determination or estimation. Some choices such as sub-model stability help associating each parameter to one bivariate projection of the copula. We also give some admissibility conditions for the considered copulas. We propose different examples as some natural multivariate extensions of Farlie-Gumbel-Morgenstern or Gumbel-Barnett.
Fichier principal
Vignette du fichier
DiBernardinoRulliere2016_Hal_v2.pdf (1.31 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01147778 , version 1 (04-05-2015)
hal-01147778 , version 2 (24-11-2016)

Licence

Paternité

Identifiants

Citer

Elena Di Bernardino, Didier Rullière. On an asymmetric extension of multivariate Archimedean copulas based on quadratic form. Dependence Modeling, 2016, Special Issue: Recent Developments in Quantitative Risk Management, 4 (1), pp.328-347. ⟨10.1515/demo-2016-0019⟩. ⟨hal-01147778v2⟩
339 Consultations
707 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More