Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-22T11:19:17.623Z Has data issue: false hasContentIssue false

BAYESIAN CHAIN LADDER MODELS

Published online by Cambridge University Press:  17 October 2014

Greg Taylor*
Affiliation:
School of Risk and Actuarial Studies, UNSW Business School, UNSW Australia, Level 6, West Lobby, UNSW Business School Building E12, UNSW Sydney 2052, Australia Tel.: +61 421 338 448, Fax: +61 2 9869 4805

Abstract

The literature on Bayesian chain ladder models is surveyed. Both Mack and cross-classified forms of the chain ladder are considered. Both cases are examined in the context of error terms distributed according to a member of the exponential dispersion family. Tweedie and over-dispersed Poisson errors follow as special cases. Bayesian cross-classified chain ladder models may randomise row, column or diagonal parameters. Column and diagonal randomisation has been largely absent from the literature until recently. The present paper allows randomisation of row and column parameters. The Bayes estimator, the linear Bayes (credibility) estimator, and the MAP estimator are shown to be identical in the Mack case, and in the cross-classified case provided that the error terms are Tweedie distributed. In the Mack case the variance structure is generalised considerably from the existing literature. In the cross-classified case the model structure differs somewhat from the existing literature, and a comparison is made between the two. MAP estimators for the cross-classified case are often given by implicit equations that require numerical solution. Recursive formulas are given for these in the general case of error terms from the exponential dispersion family. The connection between the cross-classified case and Bornhuetter-Ferguson prediction is explored.

Type
Research Article
Copyright
Copyright © ASTIN Bulletin 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bornhuetter, R. L. and Ferguson, R. E. (1972) The actuary and IBNR. Proceedings of the Casualty Actuarial Society, 67, 123184.Google Scholar
Bühlmann (1970) Mathematical Methods in Risk Theory. Berlin: Springer-Verlag.Google Scholar
England, P. D. and Verrall, R. J. (2002) Stochastic claims reserving in general insurance. British Actuarial Journal, 8 (iii), 443518.CrossRefGoogle Scholar
England, P. D., Verrall, R. J. and Wüthrich, M. V. (2012) Bayesian over-dispersed Poisson model and the Bornhuetter & Ferguson claims reserving method. Annals of Actuarial Science, 6 (2), 258281.Google Scholar
Gisler, A. and Müller, P. (2007) Credibility for additive and multiplicative models. Paper presented to the 37th ASTIN Colloquium, Orlando FL, USA. See http://www.actuaries.org/ASTIN/Colloquia/Orlando/Presentations/Gisler2.pdf.Google Scholar
Gisler, A. and Wüthrich, M. V. (2008) Credibility for the chain ladder reserving method. Astin Bulletin, 38 (2), 565597.Google Scholar
Hachemeister, C. A. and Stanard, J. N. (1975) IBNR claims count estimation with static lag functions. Spring meeting of the Casualty Actuarial Society.Google Scholar
Hertig, J. (1985) A statistical approach to the IBNR-reserves in marine insurance. Astin Bulletin, 15, 171183.CrossRefGoogle Scholar
Jewell (1974) Credible means are exact Bayesian for exponential families. Astin Bulletin, 8 (1), 7790.Google Scholar
Jewell, W. S. (1975) Regularity conditions for exact credibility. Astin Bulletin, 8 (3), 336341.Google Scholar
Jorgensen, B. and Paes de Souza, M. C. (1994) Fitting Tweedie's compound Poisson model to insurance claims data. Scandinavian Actuarial Journal, 6993.Google Scholar
Landsman, Z. and Makov, U. (1998) Exponential dispersion models and credibility. Scandinavian Actuarial Journal, 8996.Google Scholar
Mack, T. (1993) Distribution-free calculation of the standard error of chain ladder reserve estimates. Astin Bulletin, 23 (2), 213225.Google Scholar
Merz, M., Wüthrich, M. V. and Hashorva, E. (2013) Dependence modelling in multivariate claims run-off triangles. Annals of Actuarial Science, 7 (1), 325.Google Scholar
Nelder, J. A. and Wedderburn, R. W. M. (1972) Generalized linear models. Journal of the Royal Statistical Society, Series A, 135, 370384.Google Scholar
Renshaw, A. E. and Verrall, R. J. (1998) A stochastic model underlying the chain-ladder technique. British Actuarial Journal, 4 (iv), 903923.Google Scholar
Shi, P., Basu, S. and Meyers, P. P. (2012) A Bayesian log-normal model for multivariate loss reserving. North American Actuarial Journal, 16 (1), 2951.Google Scholar
Taylor, G. (2000) Loss Reserving: An Actuarial Perspective. Kluwer Academic Publishers, Boston.Google Scholar
Taylor, G. (2009) The chain ladder and Tweedie distributed claims data. Variance, 3, 96104.Google Scholar
Taylor, G. (2011) Maximum likelihood and estimation efficiency of the chain ladder. Astin Bulletin, 41 (1), 131155.Google Scholar
Tweedie, M. C. K. (1984) An index which distinguishes between some important exponential families. In Statistics: Applications and New Directions. Proceedings of the Indian Statistical Golden Jubilee International Conference (eds. Ghosh, J.K. and Roy, J.), pp. 579–604. Indian Statistical Institute, Kolkata, India.Google Scholar
Verrall, R. J. (2000) An investigation into stochastic claims reserving models and the chain-ladder technique. Insurance: Mathematics and Economics, 26 (1), 9199.Google Scholar
Verrall, R. J. (2004) A Bayesian generalised linear model for the Bornhuetter-Ferguson method of claims reserving. North American Actuarial Journal, 8 (3), 6789.Google Scholar
Wüthrich, M. V. (2007) Using a Bayesian approach for claims reserving. Variance, 1 (2), 292301.Google Scholar
Wüthrich, M. V. and Merz, M. (2008) Stochastic Claims Reserving Methods in Insurance, John Wiley & Sons Ltd, Chichester UK.Google Scholar
Wüthrich, M. V. (2012) Discussion of “A Bayesian log-normal model for multivariate loss reserving” by Shi-Basu-Meyers. North American Actuarial Journal, 16 (3), 398401.Google Scholar
Wüthrich, M. V. (2013) Challenges with non-informative gamma priors in the Bayesian over-dispersed Poisson reserving model. Insurance: Mathematics and Economics, 52, 352358.Google Scholar