Stochastic loss reserving : a new perspective from a Dirichlet model

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      <subfield code="a">Sriram, Karthik </subfield>
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      <subfield code="a">Stochastic loss reserving</subfield>
      <subfield code="b">: a new perspective from a Dirichlet model</subfield>
      <subfield code="c">Karthik Sriram, Peng Shi</subfield>
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      <subfield code="a">Forecasting the outstanding claim liabilities to set adequate reserves is critical for a nonlife insurer's solvency. ChainLadder and BornhuetterFerguson are two prominent actuarial approaches used for this task. The selection between the two approaches is often ad hoc due to different underlying assumptions. We introduce a Dirichlet model that provides a common statistical framework for the two approaches, with some appealing properties. Depending on the type of information available, the model inference naturally leads to either ChainLadder or BornhuetterFerguson prediction. Using claims data on Worker's compensation insurance from several U.S. insurers, we discuss both frequentist and Bayesian inference.</subfield>
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      <subfield code="t">The Journal of risk and insurance</subfield>
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      <subfield code="g">01/03/2021 Volumen 88 Número 1 - marzo 2021 , p. 195-230</subfield>
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