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Modèle Bayésien de tarification de l’assurance des flottes de véhicules

Author

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  • Angers, Jean-François

    (CRT)

  • Desjardins, Denise

    (CRT)

  • Dionne, Georges

    (HEC 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 model proposed 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. Nous proposons un modèle paramétrique de tarification de l’assurance de véhicules routiers appartenant à une flotte. Les tables de primes qui y sont présentées tiennent compte des accidents passés des véhicules, des caractéristiques observables des véhicules et des flottes et des infractions au Code de la sécurité routière des conducteurs et des transporteurs. De plus, les primes sont ajustées en fonction des accidents accumulés par les flottes dans le temps. Il s’agit d’un modèle qui prend directement en compte des changements explicites des différentes composantes des probabilités d’accidents. Il représente une extension aux modèles d’assurance automobile de type bonus-malus pour les primes individuelles (Lemaire, 1985 ; Dionne et Vanasse, 1989 et 1992 ; Pinquet, 1997 et 1998 ; Frangos et Vrontos, 2001 ; Purcaru et Denuit, 2003). L’extension ajoute un effet flotte à l’effet véhicule pour tenir compte des caractéristiques ou des actions non observables des transporteurs sur les taux d’accidents des camions. Cette forme de tarification comporte plusieurs avantages. Elle permet de visualiser l’impact des comportements des propriétaires des flottes et des conducteurs des véhicules sur les taux d’accidents prédits et, par conséquent, sur les primes. Elle mesure l’influence des infractions et des accidents accumulés sur les primes d’assurance mais d’une façon différente. En effet, les effets des infractions sont obtenus via la composante de régression, alors que les effets des accidents proviennent des résidus non expliqués de la régression sur les accidents des camions via un modèle bayésien de tarification.

Suggested Citation

  • 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.
  • Handle: RePEc:ris:actuec:v:80:y:2004:i:2:p:253-303
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    References listed on IDEAS

    as
    1. Angers, Jean-François & Desjardins, Denise & Dionne, Georges & Guertin, François, 2006. "Vehicle and Fleet Random Effects in a Model of Insurance Rating for Fleets of Vehicles," ASTIN Bulletin, Cambridge University Press, vol. 36(1), pages 25-77, May.
    2. 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.
    3. 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.
    4. 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.
    5. Pinquet, Jean, 1997. "Allowance for Cost of Claims in Bonus-Malus Systems," ASTIN Bulletin, Cambridge University Press, vol. 27(1), pages 33-57, May.
    6. 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.
    7. Laffont, Jean Jacques, 1997. "Collusion et information asymétrique," L'Actualité Economique, Société Canadienne de Science Economique, vol. 73(4), pages 595-609, décembre.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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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    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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