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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Veröffentlicht in:The Journal of risk and insurance Jg. 80; H. 4; S. 891 - 919
Hauptverfasser: Zhang, Yanwei, Dukic, Vanja
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Malvern Blackwell Publishing Ltd 01.12.2013
Wiley Periodicals, Inc
Blackwell
American Risk and Insurance Association, Inc
Schlagworte:
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
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Beschreibung
Zusammenfassung: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.
Bibliographie: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