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dc.contributor.authorCastaño Martínez, Antonia 
dc.contributor.authorPigueiras Voces, Gema 
dc.contributor.authorSordo Díaz, Miguel Ángel 
dc.contributor.otherEstadística e Investigación Operativaes_ES
dc.date.accessioned2024-09-30T08:25:17Z
dc.date.available2024-09-30T08:25:17Z
dc.date.issued2016
dc.identifier.issn0167-6687
dc.identifier.urihttp://hdl.handle.net/10498/33441
dc.description.abstractRisk-adjusted distributions are commonly used in actuarial science to de ne premium principles. In this paper, we claim that an appropriate risk-adjusted distribution, besides of satisfying other desirable properties, should be well-behaved under conditioning with respect to the original risk distribution. Based on a sequence of such risk-adjusted distributions, we introduce a family of premium principles that gradually incorporate the degree of risk-aversion of the insurer in the safety loading. Members of this family are particular distortion premium principles than can be represented as mixtures of TVaRs, where the weights in the mixture re ect the attitude toward risk of the insurer. We make a systematic study of this family of premium principles.es_ES
dc.formatapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherELSEVIERes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectpremium principlees_ES
dc.subjectrisk measurees_ES
dc.subjectorder statisticses_ES
dc.subjectdistortion function.es_ES
dc.titleA family of premium principles based on mixtures of TVaRses_ES
dc.typejournal articlees_ES
dc.rights.accessRightsopen accesses_ES
dc.identifier.doi10.1016/J.INSMATHECO.2016.07.006
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO//MTM2014-57559-P/ES/MODELOS PARA LA VARIABILIDAD Y LA ADMINISTRACION DEL RIESGO/ es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO//MTM2013-46962-C2-2-P/ES/DESAFIOS MATEMATICOS EN EL DISEÑO Y OPTIMIZACION DE REDES COMPLEJAS: APLICACIONES/ es_ES
dc.type.hasVersionSMURes_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
This work is under a Creative Commons License Attribution-NonCommercial-NoDerivatives 4.0 Internacional