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Linear Goal Programming and experience rating

dc.contributor.authorHeras Martínez, Antonio José
dc.contributor.authorVilar Zanón, José Luis
dc.contributor.authorGil Fana, José Antonio
dc.contributor.authorGarcía Pineda, María Pilar
dc.date.accessioned2023-06-21T01:43:12Z
dc.date.available2023-06-21T01:43:12Z
dc.date.issued2001
dc.description.abstractThis paper is devoted to the explanation of a new methodology in bonus malus system design, capable of taking into account very well known theoretical conditions like fairness and …nancial equilibrium of the portfolio, in addition to market conditions that could …t the resulting scale of premiums into competitive commercial settings. This is done through the resolution of a classical Bayesian decision problem, by means of minimization of the absolute error instead of the classical quadratic error. It is at this stage that we apply Goal Programming methods, which are linear thanks to the equivalence between the minimization of the absolute error and the minimization of the sum of some deviation variables which have a natural interpretation as rating errors. We show in an example how does the new methodology work. All the linear programs have been solved using the simplex method.
dc.description.departmentDecanato
dc.description.facultyFac. de Ciencias Económicas y Empresariales
dc.description.refereedTRUE
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/6739
dc.identifier.doib18604213
dc.identifier.issn2255-5471
dc.identifier.relatedurlhttp://www.ucm.es/centros/webs/fccee/
dc.identifier.relatedurlhttps://economicasyempresariales.ucm.es/working-papers-ccee
dc.identifier.urihttps://hdl.handle.net/20.500.14352/64414
dc.issue.number17
dc.language.isoeng
dc.page.total32
dc.publication.placeMadrid
dc.publisherFacultad de Ciencias Económicas y Empresariales. Decanato
dc.relation.ispartofseriesDocumentos de Trabajo de la Facultad de Ciencias Económicas y Empresariales
dc.rightsAtribución-NoComercial-CompartirIgual 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/3.0/es/
dc.subject.keywordProgramación lineal
dc.subject.keywordGoal Programming
dc.subject.keywordSimplexmethod
dc.subject.keywordBonus-malus system
dc.subject.keywordBayes. scale
dc.subject.keywordRating error
dc.subject.keywordBayesian decision.
dc.subject.ucmContabilidad (Economía)
dc.subject.unesco5303 Contabilidad Económica
dc.titleLinear Goal Programming and experience rating
dc.typetechnical report
dc.volume.number2001
dcterms.referencesBorgan O., Hoem J.M., Norberg R., 1981. A nonasimptotic criterion for the evaluation of automobile bonus systems. Scandinavian Actuarial Journal, 165-178. DeGroot, M., 1970. Optimal statistical decisions. McGraw-Hill, New York. Grimmett, Stirzaker, 1992. Probability and Random Processes. Second edition. Oxford University Press. Oxford. Lemaire, J., 1985. Automobile insurance: actuarial models. Kluwer. Boston. Lemaire, J., 1995. Bonus-Malus systems in automobile insurance. Kluwer. Boston. Norberg, R, 1976. A credibility theory for automobile bonus systems. Scandinavian Actuarial Journal, 92-107. Pesonen, M., 1963. A numerical method of …nding a suitable bonus scale. Astin Bulletin 2, 102-108. Romero,C., 1991. Handbook of Critical Issues in Goal Programming. Pergamon Press, Oxford. Romero,C., 1993. Teoría de la Decisión Multicriterio: Conceptos, Técnicas y Aplicaciones. Alianza Universidad Textos, Madrid. Sawaragi Y., Nakayama H., Tanino T.,1985. Theory of multiobjective optimization. Academic Press. Sundt, B., 1984. An introduction to non-life insurance mathematics. Verlag Versicherungswirtschaft, Karlsruhe. Vilar, J.L., 2000. Arithmetization of distributions and linear goal programming. Insurance: Mathematics and Economics 27, 113-122. Walhin J.F., Paris J., 1999. Using mixed Poisson processes in connection with bonus-malus systems. Astin Bulletin, Vol. 29, nº 1, 81-99.
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relation.isAuthorOfPublication.latestForDiscovery047e514e-3517-4265-8bb2-dba00d57d167

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