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Dependent Multi-Peril Ratemaking Models

Published online by Cambridge University Press:  09 August 2013

Abstract

This paper considers insurance claims that are available by cause of loss, or peril. Using this multi-peril information, we investigate multivariate frequency and severity models, emphasizing alternative dependency structures. Although dependency models may be used for many risk management strategies, we focus on ratemaking.

Motivation for this research comes from homeowners insurance and so, for the frequency portion, we consider binary response models. Specifically, we examine several multivariate binary regression models that have appeared in the biomedical literature, focusing on a dependence ratio model. For multivariate severity, we use Gaussian copulas to represent dependencies among gamma regressions.

We calibrate competing models based on a representative sample of over 400,000 records and validate them using a held-out sample of over 350,000 records. We find that methods that allow for cross-dependencies among perils provide important economic value in pricing.

Type
Research Article
Copyright
Copyright © International Actuarial Association 2010

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References

Angers, J.-F., Desjardins, D., Dionne, G. and Guertin, F. (2006) Vehicle and fleet random effects in a model of insurance rating for fleets of vehicles. Astin Bulletin 36(1), 2577.Google Scholar
Boucher, J.-P. and Denuit, M. (2006) Fixed versus random effects in Poisson regression models for claim counts. A case study with motor insurance. Astin Bulletin 36(1), 285301.Google Scholar
Boucher, J.-P. and Denuit, M. (2008) Credibility premiums for the zero-inflated Poisson model and new hunger for bonus interpretation. Insurance: Mathematics and Economics 42(2), 727735.Google Scholar
Bowers, N.L., Gerber, H.U., Hickman, J.C., Jones, D.A. and Nesbitt, C.J. (1997) Actuarial Mathematics. Society of Actuaries, Schaumburg, IL.Google Scholar
Carey, V., Zeger, S.L. and Diggle, P. (1993) Modelling multivariate binary data with alternating logistic regressions. Biometrika 80(3), 517526.Google Scholar
Diggle, P.J., Heagerty, P., Liang, K.-Y. and Zeger, S.L. (2002) Analysis of Longitudinal Data, Second Edition. Oxford University Press.Google Scholar
Ekholm, A., Smith, P.W.F. and McDonald, J.W. (1995) Marginal regression analysis of a multivariate binary response. Biometrika 82(4), 847854.Google Scholar
Ekholm, A., McDonald, J.W. and Smith, P.W.F. (2000) Association models for a multivariate binary response. Biometrics 56, 712718.Google Scholar
Frees, E.W. and Wang, P. (2005) Credibility using copulas. North American Actuarial Journal 9(2), 3148.CrossRefGoogle Scholar
Frees, E.W. and Valdez, E. (1998) Understanding relationships using copulas. North American Actuarial Journal 2(1), 125.Google Scholar
Frees, E.W., Shi, P. and Valdez, E.A. (2009) Actuarial applications of a hierarchical insurance claims model. Astin Bulletin 39(1), 165197.Google Scholar
Hastie, T., Tibshirani, R. and Friedman, J. (2001) The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, New York.Google Scholar
Liang, K.-Y. and Zeger, S.L. (1992) Multivariate regression analyses for categorical data. Journal of the Royal Statistical Society B 54(1), 340.Google Scholar
Lo, C.H., Fung, W.K. and Zhu, Z.Y. (2007) Structural parameter estimation using generalized estimating equations for regression credibility models. Astin Bulletin 37(2), 323343.Google Scholar
Mahmoudvand, R. and Hassani, H. (2009) Generalized bonus-malus systems with a frequency and a severity component on an individual basis in automobile insurance. Astin Bulletin 39(1), 307315.Google Scholar
Modlin, C. (2005) Homeowners' modeling. Presentation at the 2005 Casualty Actuarial Society Seminar on Predictive Modeling, available at http://www.casact.org/education/specsem/f2005/handouts/modlin.pdf.Google Scholar
Zhao, L.P. and Prentice, R.L. (1990) Correlated binary regression using a quadratic exponential model. Biometrika 77, 642648.Google Scholar