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Vehicle and fleet random effects in a model of insurance rating for fleets of vehicles

Author

Listed:
  • Angers, Jean-François

    (Université de Montréal)

  • Desjardins, Denise

    (HEC Montreal, Canada Research Chair in Risk Management)

  • Dionne, Georges

    (HEC Montreal, Canada Research Chair in Risk Management)

  • Guertin, François

    (Université de Montréal)

Abstract

We are proposing a parametric model to rate insurance for vehicles belonging to a fleet. The tables of premiums presented take into account past vehicle accidents, observable characteristics of the vehicles and fleets, and violations of the road-safety code committed by drivers and carriers. The premiums are also adjusted according to accidents accumulated by the fleets over time. The proposed model accounts directly for explicit changes in the various components of the probability of accidents. It represents an extension of bonus malus-type automobile insurance models for individual premiums (Lemaire, 1985; Dionne and Vanasse, 1989 and 1992; Pinquet, 1997 and 1998; Frangos and Vrontos, 2001; Purcaru and Denuit, 2003). The extension adds a fleet effect to the vehicle effect so as to account for the impact that the unobservable characteristics or actions of carriers can have on truck accident rates. This form of rating makes it possible to visualize what impact the behaviors of owners and drivers can have on the predicted rate of accidents and, consequently, on premiums. The results are compared to those of the semiparametric approach.

Suggested Citation

  • Angers, Jean-François & Desjardins, Denise & Dionne, Georges & Guertin, François, 2005. "Vehicle and fleet random effects in a model of insurance rating for fleets of vehicles," Working Papers 04-7, HEC Montreal, Canada Research Chair in Risk Management.
    • Handle: RePEc:ris:crcrmw:2004_007
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    References listed on IDEAS

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    1. Dionne, Georges & Vanasse, Charles, 1989. "A Generalization of Automobile Insurance Rating Models: The Negative Binomial Distribution with a Regression Component," ASTIN Bulletin, Cambridge University Press, vol. 19(2), pages 199-212, November.
    2. Pinquet, Jean, 1998. "Designing Optimal Bonus-Malus Systems from Different Types of Claims," ASTIN Bulletin, Cambridge University Press, vol. 28(2), pages 205-220, November.
    3. John M. Abowd & Francis Kramarz & David N. Margolis, 1999. "High Wage Workers and High Wage Firms," Econometrica, Econometric Society, vol. 67(2), pages 251-334, March.
    4. Pinquet, Jean, 1997. "Allowance for Cost of Claims in Bonus-Malus Systems," ASTIN Bulletin, Cambridge University Press, vol. 27(1), pages 33-57, May.
    5. Desjardins, Denise & Dionne, Georges & Pinquet, Jean, 2001. "Experience Rating Schemes for Fleets of Vehicles," ASTIN Bulletin, Cambridge University Press, vol. 31(1), pages 81-105, May.
    6. Teugels, Jozef L. & Sundt, Bjorn, 1991. "A stop-loss experience rating scheme for fleets of cars," Insurance: Mathematics and Economics, Elsevier, vol. 10(3), pages 173-179, December.
    7. Dionne, G & Vanasse, C, 1992. "Automobile Insurance Ratemaking in the Presence of Asymmetrical Information," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(2), pages 149-165, April-Jun.
    8. Dionne, G. & Desjardins, D. & Ingabire, M.-G. & Aqdim, R., 2001. "La perception du risque d'etre arrete chez les camionneurs et transporteurs routiers," Ecole des Hautes Etudes Commerciales de Montreal- 01-03, Ecole des Hautes Etudes Commerciales de Montreal-Chaire de gestion des risques..
    9. Hausman, Jerry & Hall, Bronwyn H & Griliches, Zvi, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," Econometrica, Econometric Society, vol. 52(4), pages 909-938, July.
    10. Dionne, Georges & Laberge-Nadeau, Claire & Desjardins, Denise & Messier, Stéphane & Maag, Urs, 1998. "Analysis of the economic impact of medical and optometric driving standards on costs incurred by trucking firms and on the social costs of traffic accidents," Working Papers 98-6, HEC Montreal, Canada Research Chair in Risk Management.
    11. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Applications to Poisson Models," Econometrica, Econometric Society, vol. 52(3), pages 701-720, May.
    12. Frangos, Nicholas E. & Vrontos, Spyridon D., 2001. "Design of Optimal Bonus-Malus Systems With a Frequency and a Severity Component On an Individual Basis in Automobile Insurance," ASTIN Bulletin, Cambridge University Press, vol. 31(1), pages 1-22, May.
    13. Leon N. Moses & Ian Savage, 1996. "Identifying Dangerous Trucking Firms," Risk Analysis, John Wiley & Sons, vol. 16(3), pages 359-366, June.
    14. Purcaru, Oana & Denuit, Michel, 2003. "Dependence in Dynamic Claim Frequency Credibility Models," ASTIN Bulletin, Cambridge University Press, vol. 33(1), pages 23-40, May.
    15. J. Pinquet, 1997. "Experience rating through heterogeneous models," THEMA Working Papers 97-25, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
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    Cited by:

    1. Denise Desjardins & Georges Dionne & Yang Lu, 2023. "Hierarchical random‐effects model for the insurance pricing of vehicles belonging to a fleet," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 242-259, March.
    2. Angers, Jean-François & Desjardins, Denise & Dionne, Georges, 2004. "Modèle Bayésien de tarification de l’assurance des flottes de véhicules," L'Actualité Economique, Société Canadienne de Science Economique, vol. 80(2), pages 253-303, Juin-Sept.
    3. Wojciech Bijak, 2015. "Merging and aggregation of bonus-malus systems in automobile insurance," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 37, pages 127-154.
    4. Jean-François Angers & Denise Desjardins & Georges Dionne & Benoit Dostie & François Guertin, 2007. "Poisson Models with Employer-Employee Unobserved Heterogeneity: An Application to Absence Data," Cahiers de recherche 07-02, HEC Montréal, Institut d'économie appliquée.
    5. Angers, Jean-François & Desjardins, Denise & Dionne, Georges & Guertin, François, 2018. "Modelling And Estimating Individual And Firm Effects With Count Panel Data," ASTIN Bulletin, Cambridge University Press, vol. 48(3), pages 1049-1078, September.
    6. Dionne, Georges & Desjardins, Denise & Angers, Jean-François, 2021. "Road safety for fleets of vehicles," Working Papers 21-3, HEC Montreal, Canada Research Chair in Risk Management.
    7. Marcin Owczarczuk & Damian Przekop, 2014. "Accidents of company cars. A full service leasing company’s perspective," Applied Econometrics Papers, Department of Applied Econometrics, Warsaw School of Economics, vol. 1(1), pages 39-63.
    8. Levon Barseghyan & Francesca Molinari & Darcy Steeg Morris & Joshua C. Teitelbaum, 2020. "The Cost of Legal Restrictions on Experience Rating," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 17(1), pages 38-70, March.
    9. Jean Pinquet, 2012. "Experience rating in non-life insurance," Working Papers hal-00677100, HAL.

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    More about this item

    Keywords

    Fleet of vehicles; random effects; vehicle effect; fleet effect; insurance rating; behaviors of owners and drivers; Poisson; gamma; Dirichlet; parametric model;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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