Displaying similar documents to “On uniform tail expansions of bivariate copulas”

On uniform tail expansions of multivariate copulas and wide convergence of measures

Piotr Jaworski (2006)

Applicationes Mathematicae

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The theory of copulas provides a useful tool for modeling dependence in risk management. In insurance and finance, as well as in other applications, dependence of extreme events is particularly important, hence there is a need for a detailed study of the tail behaviour of multivariate copulas. We investigate the class of copulas having regular tails with a uniform expansion. We present several equivalent characterizations of uniform tail expansions. Next, basing on them, we determine...

Characterizations of bivariate conic, extreme value, and Archimax copulas

Susanne Saminger-Platz, José De Jesús Arias-García, Radko Mesiar, Erich Peter Klement (2017)

Dependence Modeling

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Based on a general construction method by means of bivariate ultramodular copulas we construct, for particular settings, special bivariate conic, extreme value, and Archimax copulas. We also show that the sets of copulas obtained in this way are dense in the sets of all conic, extreme value, and Archimax copulas, respectively.

Quantifying the impact of different copulas in a generalized CreditRisk + framework An empirical study

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...

Constructing copulas by means of pairs of order statistics

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.

On Conditional Value at Risk (CoVaR) for tail-dependent copulas

Piotr Jaworski (2017)

Dependence Modeling

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The paper deals with Conditional Value at Risk (CoVaR) for copulas with nontrivial tail dependence. We show that both in the standard and the modified settings, the tail dependence function determines the limiting properties of CoVaR as the conditioning event becomes more extreme. The results are illustrated with examples using the extreme value, conic and truncation invariant families of bivariate tail-dependent copulas.

Invariant copulas

Erich Peter Klement, Radko Mesiar, Endre Pap (2002)

Kybernetika

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My introduction to copulas

Fabrizio Durante, Giovanni Puccetti, Matthias Scherer, Steven Vanduffel (2017)

Dependence Modeling

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Univariate conditioning of copulas

Radko Mesiar, Vladimír Jágr, Monika Juráňová, Magda Komorníková (2008)

Kybernetika

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The univariate conditioning of copulas is studied, yielding a construction method for copulas based on an a priori given copula. Based on the gluing method, g-ordinal sum of copulas is introduced and a representation of copulas by means of g-ordinal sums is given. Though different right conditionings commute, this is not the case of right and left conditioning, with a special exception of Archimedean copulas. Several interesting examples are given. Especially, any Ali-Mikhail-Haq copula...