Editorial to the special issue on “Random Variables, Joint Distribution Functions, and Copulas”
Fabrizio Durante, Radko Mesiar, Carlo Sempi (2008)
Kybernetika
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Fabrizio Durante, Radko Mesiar, Carlo Sempi (2008)
Kybernetika
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Ali Dolati, Manuel Úbeda-Flores (2009)
Kybernetika
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In this paper, we introduce two transformations on a given copula to construct new and recover already-existent families. The method is based on the choice of pairs of order statistics of the marginal distributions. Properties of such transformations and their effects on the dependence and symmetry structure of a copula are studied.
Kevin Jakob, Matthias Fischer (2014)
Dependence Modeling
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Without any doubt, credit risk is one of the most important risk types in the classical banking industry. Consequently, banks are required by supervisory audits to allocate economic capital to cover unexpected future credit losses. Typically, the amount of economical capital is determined with a credit portfolio model, e.g. using the popular CreditRisk+ framework (1997) or one of its recent generalizations (e.g. [8] or [15]). Relying on specific distributional assumptions, the credit...
Fabrizio Durante, Giovanni Puccetti, Matthias Scherer, Steven Vanduffel (2017)
Dependence Modeling
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Elena Di Bernardino, Didier Rullière (2016)
Dependence Modeling
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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...