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Predicting multivariate insurance loss payments under the bayesian copula framework

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      <subfield code="a">Zhang, Yanwei</subfield>
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      <subfield code="a">Predicting multivariate insurance loss payments under the bayesian copula framework</subfield>
      <subfield code="c">Yanwei Zhang, Vanja Dukic</subfield>
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      <subfield code="a">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.</subfield>
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      <subfield code="w">MAP20077000727</subfield>
      <subfield code="t">The Journal of risk and insurance</subfield>
      <subfield code="d">Nueva York : The American Risk and Insurance Association, 1964-</subfield>
      <subfield code="x">0022-4367</subfield>
      <subfield code="g">02/12/2013 Volumen 80 Número 4 - diciembre 2013 </subfield>
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      <subfield code="y">MÁS INFORMACIÓN</subfield>
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