Displaying similar documents to “Joint weak hazard rate order under non-symmetric copulas”

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.

Componentwise concave copulas and their asymmetry

Fabrizio Durante, Pier Luigi Papini (2009)

Kybernetika

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The class of componentwise concave copulas is considered, with particular emphasis on its closure under some constructions of copulas (e.g., ordinal sum) and its relations with other classes of copulas characterized by some notions of concavity and/or convexity. Then, a sharp upper bound is given for the L ∞ -measure of non-exchangeability for copulas belonging to this class.

Invariant copulas

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

Kybernetika

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Extreme distribution functions of copulas

Manuel Úbeda-Flores (2008)

Kybernetika

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In this paper we study some properties of the distribution function of the random variable C(X,Y) when the copula of the random pair (X,Y) is M (respectively, W) – the copula for which each of X and Y is almost surely an increasing (respectively, decreasing) function of the other –, and C is any copula. We also study the distribution functions of M(X,Y) and W(X,Y) given that the joint distribution function of the random variables X and Y is any copula.

My introduction to copulas

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

Dependence Modeling

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A two-component copula with links to insurance

S. Ismail, G. Yu, G. Reinert, T. Maynard (2017)

Dependence Modeling

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This paper presents a new copula to model dependencies between insurance entities, by considering how insurance entities are affected by both macro and micro factors. The model used to build the copula assumes that the insurance losses of two companies or lines of business are related through a random common loss factor which is then multiplied by an individual random company factor to get the total loss amounts. The new two-component copula is not Archimedean and it extends the toolkit...