Predicting multivariate insurance loss payments under the bayesian copula framework

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<dc:creator>Zhang, Yanwei</dc:creator>
<dc:date>2013-12-02</dc:date>
<dc:description xml:lang="es">Sumario: The literature of predicting the outstanding liability for insurance companies has undergone rapid and profound changes in the past three decades, most recently focusing on Bayesian stochastic modeling and multivariate insurance loss payments. In this article, we introduce a novel Bayesian multivariate model based on the use of parametric copula to account for dependencies between various lines of insurance claims. We derive a full Bayesian stochastic simulation algorithm that can estimate parameters in this class of models. We provide an extensive discussion of this modeling framework and give examples that deal with a wide range of topics encountered in the multivariate loss prediction settings.</dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/145873.do</dc:identifier>
<dc:language>spa</dc:language>
<dc:rights xml:lang="es">InC - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">Predicting multivariate insurance loss payments under the bayesian copula framework</dc:title>
<dc:relation xml:lang="es">En: The Journal of risk and insurance. - Nueva York : The American Risk and Insurance Association, 1964- = ISSN 0022-4367. - 02/12/2013 Volumen 80 Número 4 - diciembre 2013 </dc:relation>
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