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Fat-Tailed regression modeling with spliced distributions

Recurso electrónico / Electronic resource
Registro MARC
Tag12Valor
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001  MAP20190014168
003  MAP
005  20190520164914.0
008  190517e20181203esp|||p |0|||b|spa d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎6
1001 ‎$0‎MAPA20140000234‎$a‎Gan, Guojun
24510‎$a‎Fat-Tailed regression modeling with spliced distributions‎$c‎Guojun Gao
520  ‎$a‎Insurance claims data usually contain a large number of zeros and exhibits fat-tail behavior. Misestimation of one end of the tail impacts the other end of the tail of the claims distribution and can affect both the adequacy of premiums and needed reserves to hold. In addition, insured policyholders in a portfolio are naturally non-homogeneous. It is an ongoing challenge for actuaries to be able to build a predictive model that will simultaneously capture these peculiar characteristics of claims data and policyholder heterogeneity. Such models can help make improved predictions and thereby ease the decision-making process. This article proposes the use of spliced regression models for fitting insurance loss data. A primary advantage of spliced distributions is their flexibility to accommodate modeling different segments of the claims distribution with different parametric models. The threshold that breaks the segments is assumed to be a parameter, and this presents an additional challenge in the estimation. Our simulation study demonstrates the effectiveness of using multistage optimization for likelihood inference and at the same time the repercussions of model misspecification. For purposes of illustration, we consider three-component spliced regression models: the first component contains zeros, the second component models the middle segment of the loss data, and the third component models the tail segment of the loss data. We calibrate these proposed models and evaluate their performance using a Singapore auto insurance claims dataset. The estimation results show that the spliced regression model performs better than the Tweedie regression model in terms of tail fitting and prediction accuracy.
650 4‎$0‎MAPA20080592011‎$a‎Modelos actuariales
650 4‎$0‎MAPA20080567118‎$a‎Reclamaciones
650 4‎$0‎MAPA20080601799‎$a‎Gestión de siniestros
650 4‎$0‎MAPA20080592059‎$a‎Modelos predictivos
7730 ‎$w‎MAP20077000239‎$t‎North American actuarial journal‎$d‎Schaumburg : Society of Actuaries, 1997-‎$x‎1092-0277‎$g‎03/12/2018 Tomo 22 Número 4 - 2018 , p. 554-573