Predicting Multivariate Insurance Loss Payments Under the Bayesian Copula Framework

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 multivari...

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Vydáno v:	The Journal of risk and insurance Ročník 80; číslo 4; s. 891 - 919
Hlavní autoři:	Zhang, Yanwei, Dukic, Vanja
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Malvern Blackwell Publishing Ltd 01.12.2013 Wiley Periodicals, Inc Blackwell American Risk and Insurance Association, Inc
Témata:	Accidents Actuarial science Analysis Automobile insurance Bayes-Statistik Bayesian analysis Bayesian statistical decision theory Copula functions Economic aspects Evaluation Financial analysis Generalized linear model Industry forecasts Insurance claims Insurance companies Insurance industry Liability (Law) Liability insurance Loss reserves Management Methods Modeling Multivariate analysis Multivariate Verteilung Parametric models Predictive modeling Property & casualty insurance Property and casualty insurance Risikomodell Schadenversicherung Statistical inference Trends Verlustverrechnung Workers compensation insurance United States
ISSN:	0022-4367, 1539-6975
On-line přístup:	Získat plný text
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Shrnutí:	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.
Bibliografie:	istex:FE4F104992EC49660D14B8A768535944089DC5B1 ArticleID:JORI1480 ark:/67375/WNG-SZKL3JWJ-M SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14
ISSN:	0022-4367 1539-6975
DOI:	10.1111/j.1539-6975.2012.01480.x