Provisions for claims outstanding, incurred but not reported, with generalized linear models: prediction error formulated according to calendar year

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Published 18-09-2018
Eva Boj del Val Teresa Costa Cor

Abstract

In the current context of Solvency II, insurance companies are required to implement demanding business risk management systems. An important aspect of this risk management is the problem of technical provisions in non-life insurance and, as such, it is in the interest of insurers to calculate the prediction error that has occurred when using methodology to estimate a company's future payments. Furthermore, the predictive distribution of the fitted values, which is descriptive of the risk, allows us to estimate, for example, its Value at Risk at a given confi­dence level. In this paper we focus on the application of generalized linear models to the amounts of claim losses of a run-off triangle. In order to achieve error distribution, a parameter dependent parametric family is assumed, along with the logarithmic link function. The parametric family has as particular cases the Poisson, the Gamma and the Inverse Gaussian distributions. The particular case which assumes an (over-dispersed) Poisson distribu­tion with the logarithmic link is widely known because it offers the same provision estimation as the deterministic Chain-Ladder method. In this study we develop formulas of the prediction error of future payments by calendar years for the general parametric family. This allows us to perform calculations that consider a financial environ­ment, whether employing analytical formulation or bootstrap estimation. In practice, the presented formulations allow a determination to be made of the present value of the incurred but not reported claim of future payments including a risk margin with statistical significance.

How to Cite

Boj del Val, E., & Costa Cor, T. (2018). Provisions for claims outstanding, incurred but not reported, with generalized linear models: prediction error formulated according to calendar year. Cuadernos De Gestión, 17(2), 157–174. https://doi.org/10.5295/cdg.150526eb
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Keywords

Technical provisions, generalized linear model, calendar year, Solvency II

References
Albarrán, I. and Alonso, P., 2010. Métodos estocásticos de estimaciones de las provisiones técnicas en el marco de Solvencia II. Cuadernos de la Fundación MAPFRE, 158. Madrid: Fundación MAPFRE Estudios.
Boj, E., Claramunt, M. M. and Fortiana, J., 2004. Análisis multivariante aplicado a la selección de factores de riesgo en la tarificación. Cuadernos de la Fundación MAPFRE, 88. Madrid: Fundación MAPFRE Estudios.
Boj, E. and Costa, T., 2014. Modelo lineal generalizado y cálculo de la provisión técnica. Depósito digital de la Universidad de Barcelona. Colección de objetos y materiales docentes (OMADO). http://hdl.handle.net/2445/49068
Boj, E., Costa, T. and Espejo, J., 2014. Provisiones técnicas por años de calendario mediante modelo lineal generalizado. Una aplicación con RExcel. Anales del Instituto de Actuarios Españoles, Tercera Época, 20, 83–116.
Efron, B. and Tibshirani, J., 1998. An Introduction to the bootstrap. New York: Chapman & Hall/CRC.
England, P. D. and Verrall, R. J., 1999. Analytic and bootstrap estimates of prediction errors in claims reserving. Insurance: Mathematics and Economics, 25, 281–293.
England, P. D., 2002. Addendum to “Analytic and bootstrap estimates of prediction errors in claim reserving. Insurance: Mathematics and Economics, 31, 461–466.
England, P. D. and Verrall, R. J., 2002. Stochastic claims reserving in general insurance (with discussion). British Actuarial Journal, 8, 443–544.
England, P. D. and Verrall, R. J., 2006. Predictive distributions of outstanding liabilities in general insurance. Annals of Actuarial Science, 1 (II), 221–270.
Espejo, J., Boj, E. and Costa, T., 2014. Una aplicación de RExcel para el cálculo de provisiones técnicas con modelo lineal generalizado. Depósito digital de la Universidad de Barcelona. Colección de Investigación- Software. http://hdl.handle.net/2445/56230
Haberman, S. and Renshaw, A. E., 1996. Generalized linear models and actuarial science. Journal of the Royal Statistical Society. Series D (The Statistician), 45 (4), 407–436.
Kaas, R., Goovaerts, M., Dhaene, J. and Denuit, M., 2008. Modern actuarial risk theory: using R. Second edition. Heidelberg: Springer-Verlag.
Moreno, F. P., 2013. Jornada sobre las Directrices de EIOPA de preparación a Solvencia II. Dirección General de Seguros y Fondos de Pensiones. http://www.dgsfp.mineco.es/sector/documentos/Jornada%20Directrices%20EIOPA%20de%20preparacion%20Solvencia%20II_10-12-2013/Fernando%20Moreno_Jornada%20Directrices%20EIOPA.%20DGSFP-UNESPA.pdf
McCullagh, P. and Nelder, J., 1989. Generalized linear models. Second edition. Londres: Chapman and Hall.
Parlamento Europeo y Consejo de la Unión Europea, 2009. Directiva 2009/138/CE del Parlamento Europeo y del Consejo, de 25 de noviembre de 2009. Diario Oficial de la Unión Europea, L 335, 1–155. http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2009:335:0001:0155:es:PDF
R Development Core Team, 2016. R: a language and environment for statistical computing. Vienna. Austria. http://www.R-project.org/
Renshaw, A.E., 1989. Chain ladder and interactive modelling (claims reserving and GLIM). Journal of the Institute of Actuaries, 116 (III), 559–587.
Renshaw, A. E., 1994. On the second moment properties and the implementation of certain GLIM based stochastic claims reserving models. Actuarial Research Paper, No. 65. Department of Actuarial Science and Statistics, City University, London.
Taylor, G. and Ashe, F.R., 1983. Second Moments of Estimates of Outstanding Claims. Journal of Econometrics, 23, 37–61.
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