Stochastic loss reserving : a new perspective from a Dirichlet model

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MARC record
LDR  00000cab a2200000 4500
001  MAP20210005695
003  MAP
005  20210302165657.0
008  210219e20210301usa|||p |0|||b|eng d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎6
100  ‎$0‎MAPA20210003219‎$a‎Sriram, Karthik
24510‎$a‎Stochastic loss reserving‎$b‎: a new perspective from a Dirichlet model‎$c‎Karthik Sriram, Peng Shi
520  ‎$a‎Forecasting 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.
650 4‎$0‎MAPA20080586447‎$a‎Modelo estocástico
650 4‎$0‎MAPA20080590567‎$a‎Empresas de seguros
650 4‎$0‎MAPA20080573935‎$a‎Seguros no vida
650 4‎$0‎MAPA20080567118‎$a‎Reclamaciones
7001 ‎$0‎MAPA20100048726‎$a‎Shi, Peng
7730 ‎$w‎MAP20077000727‎$t‎The Journal of risk and insurance‎$d‎Nueva York : The American Risk and Insurance Association, 1964-‎$x‎0022-4367‎$g‎01/03/2021 Volumen 88 Número 1 - marzo 2021 , p. 195-230