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A Likelihood Approach to Bornhuetter–Ferguson Analysis

Author

Listed:
  • Valandis Elpidorou

    (Arch Reinsurance Europe Underwriting dac Ireland, Dublin 4, Ireland)

  • Carolin Margraf

    (Cass Business School, University of London, London EC1Y 8TZ, UK)

  • María Dolores Martínez-Miranda

    (Department of Statistics and Operations Research, University of Granada, 18071 Granada, Spain)

  • Bent Nielsen

    (Nuffield College, University of Oxford, Oxford OX1 1NF, UK)

Abstract

A new Bornhuetter–Ferguson method is suggested herein. This is a variant of the traditional chain ladder method. The actuary can adjust the relative ultimates using externally estimated relative ultimates. These correspond to linear constraints on the Poisson likelihood underpinning the chain ladder method. Adjusted cash flow estimates were obtained as constrained maximum likelihood estimates. The statistical derivation of the new method is provided in the generalised linear model framework. A related approach in the literature, combining unconstrained and constrained maximum likelihood estimates, is presented in the same framework and compared theoretically. A data illustration is described using a motor portfolio from a Greek insurer.

Suggested Citation

  • Valandis Elpidorou & Carolin Margraf & María Dolores Martínez-Miranda & Bent Nielsen, 2019. "A Likelihood Approach to Bornhuetter–Ferguson Analysis," Risks, MDPI, vol. 7(4), pages 1-20, December.
  • Handle: RePEc:gam:jrisks:v:7:y:2019:i:4:p:119-:d:296216
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    References listed on IDEAS

    as
    1. Heberle, Jochen & Thomas, Anne, 2016. "The fuzzy Bornhuetter–Ferguson method: an approach with fuzzy numbers," Annals of Actuarial Science, Cambridge University Press, vol. 10(2), pages 303-321, September.
    2. Mack, Thomas, 1991. "A Simple Parametric Model for Rating Automobile Insurance or Estimating IBNR Claims Reserves," ASTIN Bulletin, Cambridge University Press, vol. 21(1), pages 93-109, April.
    3. Maria Martínez-Miranda & Jens Nielsen & Richard Verrall, 2013. "Double Chain Ladder and Bornhuetter-Ferguson," North American Actuarial Journal, Taylor & Francis Journals, vol. 17(2), pages 101-113.
    4. Nataliya Chukhrova & Arne Johannssen, 2017. "State Space Models and the K alman -Filter in Stochastic Claims Reserving: Forecasting, Filtering and Smoothing," Risks, MDPI, vol. 5(2), pages 1-23, May.
    5. Kevin Kuo, 2018. "DeepTriangle: A Deep Learning Approach to Loss Reserving," Papers 1804.09253, arXiv.org, revised Sep 2019.
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    11. Bühlmann, Hans & Moriconi, Franco, 2015. "Credibility Claims Reserving With Stochastic Diagonal Effects," ASTIN Bulletin, Cambridge University Press, vol. 45(2), pages 309-353, May.
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