The class of multivariate Archimedean copulas is defined by using a real-valued function called the generator of the copula. This generator satisfies some properties, including d-monotonicity. We propose here a new basic transformation of this generator, preserving these properties, thus ensuring the validity of the transformed generator and inducing a proper valid copula. This transformation acts only on a specific portion of the generator, it allows both the non-reduction of the likelihood on a given dataset, and the choice of the upper tail dependence coefficient of the transformed copula. Numerical illustrations show the utility of this construction, which can improve the fit of a given copula both on its central part and its tail
Tail dependence copulas provide a natural perspective from which one can study the dependence in the...
Archimedean copulas are one of the most known classes of copulas. They allow modeling the dependenci...
Copulas are distribution functions with standard uniform univariate margins. One particular parametr...
The class of multivariate Archimedean copulas is defined by using a real-valued function called the ...
International audienceWe study the impact of certain transformations within the class of Archimedean...
AbstractExplicit functional forms for the generator derivatives of well-known one-parameter Archimed...
A complete and user-friendly directory of tails of Archimedean copulas is presented which can be use...
AbstractA complete and user-friendly directory of tails of Archimedean copulas is presented which ca...
This paper introduces a new family of multivariate copula functions defined by two generators, which...
International audienceThis paper presents the impact of a class of transformations of copulas in the...
Two simulation algorithms for hierarchical Archimedean copulas in the case when intra-group generato...
An important topic in Quantitative Risk Management concerns the modeling of dependence among risk so...
International audienceAn important topic in Quantitative Risk Management concerns the modeling of de...
Tail dependence copulas provide a natural perspective from which one can study the dependence in the...
summary Recently, Liebscher (2006) introduced a general construction scheme of d-variate copulas whi...
Tail dependence copulas provide a natural perspective from which one can study the dependence in the...
Archimedean copulas are one of the most known classes of copulas. They allow modeling the dependenci...
Copulas are distribution functions with standard uniform univariate margins. One particular parametr...
The class of multivariate Archimedean copulas is defined by using a real-valued function called the ...
International audienceWe study the impact of certain transformations within the class of Archimedean...
AbstractExplicit functional forms for the generator derivatives of well-known one-parameter Archimed...
A complete and user-friendly directory of tails of Archimedean copulas is presented which can be use...
AbstractA complete and user-friendly directory of tails of Archimedean copulas is presented which ca...
This paper introduces a new family of multivariate copula functions defined by two generators, which...
International audienceThis paper presents the impact of a class of transformations of copulas in the...
Two simulation algorithms for hierarchical Archimedean copulas in the case when intra-group generato...
An important topic in Quantitative Risk Management concerns the modeling of dependence among risk so...
International audienceAn important topic in Quantitative Risk Management concerns the modeling of de...
Tail dependence copulas provide a natural perspective from which one can study the dependence in the...
summary Recently, Liebscher (2006) introduced a general construction scheme of d-variate copulas whi...
Tail dependence copulas provide a natural perspective from which one can study the dependence in the...
Archimedean copulas are one of the most known classes of copulas. They allow modeling the dependenci...
Copulas are distribution functions with standard uniform univariate margins. One particular parametr...