On the invariant properties of notions of positive dependence and copulas under increasing transformations

DOI10.1016/J.INSMATHECO.2011.10.003zbMath1239.91074OpenAlexW2030678395MaRDI QIDQ2427805

Wei Wei, Jun Cai

Publication date: 18 April 2012

Published in: Insurance Mathematics \& Economics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.insmatheco.2011.10.003

zbMATH Keywords

copulapositive dependencedependent risksurvival copulageneralized inverse functionconditional increaseconditional increase in sequencepositive dependence through stochastic orderingpositive dependence through upper orthant ordering

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Measures of association (correlation, canonical correlation, etc.) (62H20)

Related Items (10)

A positive dependence notion based on componentwise unimodality of copulas ⋮ Preservation of increasing convex/concave order under the formation of parallel/series system of dependent components ⋮ Stochastic orders and multivariate measures of risk contagion ⋮ Systemic risk: conditional distortion risk measures ⋮ Stochastic orders and co-risk measures under positive dependence ⋮ Probability equivalent level for CoVaR and VaR ⋮ Optimal Reinsurance Under the Risk-Adjusted Value of an Insurer’s Liability and an Economic Reinsurance Premium Principle ⋮ On asset allocation for a threshold model with dependent returns ⋮ A criterion for the comparison of binary classifiers based on a stochastic dominance with an application to the sale of home insurances ⋮ Comparison of conditional distributions in portfolios of dependent risks

Uses Software

• QRM

Cites Work

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• A characterization of the multivariate excess wealth ordering
• An introduction to copulas.
• Stochastic orders
• A concept of negative dependence using stochastic ordering
• Archimedean copulae and positive dependence
• Some notions of multivariate positive dependence
• A Family of Concepts of Dependence for Bivariate Distributions

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