Risky Loss Distributions and Modeling the Loss Reserve Pay-out Tail
AbstractAlthough an extensive literature has developed on modeling the loss reserve runoff triangle, the estimation of severity distributions applicable to claims settled in specific cells of the runoff triangle has received little attention in the literature. This paper proposes the use of a very flexible probability density function, the generalized beta of the 2nd kind (GB2) to model severity distributions in the cells of the runoff triangle and illustrates the use of the GB2 based on a sample of nearly 500,000 products liability paid claims. The results show that the GB2 provides a significantly better fit to the severity data than conventional distributions such as the Weibull, Burr 12, and generalized gamma and that modeling severity by cell is important to avoid errors in estimating the riskiness of liability claims payments, especially at the longer lags.
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Bibliographic InfoArticle provided by Review of Applied Economics in its journal Review of Applied Economics.
Volume (Year): 3 (2007)
Issue (Month): 1-2 ()
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Loss distributions; loss reserves; generalized beta distribution; liability insurance; Risk and Uncertainty; C16; G22;
Find related papers by JEL classification:
- C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
- G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
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- Alice X. D. Dong & Jennifer S. K. Chan & Gareth W. Peters, 2014. "Risk Margin Quantile Function Via Parametric and Non-Parametric Bayesian Quantile Regression," Papers 1402.2492, arXiv.org.
- Dong, A.X.D. & Chan, J.S.K., 2013. "Bayesian analysis of loss reserving using dynamic models with generalized beta distribution," Insurance: Mathematics and Economics, Elsevier, vol. 53(2), pages 355-365.
- James Hansen & James McDonald & Panayiotis Theodossiou & Brad Larsen, 2010. "Partially Adaptive Econometric Methods For Regression and Classification," Computational Economics, Society for Computational Economics, vol. 36(2), pages 153-169, August.
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