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The Prediction Error of Bornhuetter/Ferguson

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  • Mack, Thomas

Abstract

Together with the Chain Ladder (CL) method, the Bornhuetter/Ferguson (BF) method is one of the most popular claims reserving methods. Whereas a formula for the prediction error of the CL method has been published already in 1993, there is still nothing equivalent available for the BF method. On the basis of the BF reserve formula, this paper develops a stochastic model for the BF method. From this model, a formula for the prediction error of the BF reserve estimate is derived.Moreover, the model gives important advice on how to estimate the parameters for the BF reserve formula. E.g. it turns out that the appropriate BF development pattern is different from the CL pattern. This is a nice add-on as it makes BF to a standalone reserving method which is fully independent from CL. The other parameter required for the BF reserve is the well-known initial estimate for the ultimate claims amount. Here the stochastic model clearly shows what has to be meant with ‘initial’.In order to apply the formula for the prediction error, the actuary must assess his uncertainty about both sets of parameters, about the development pattern and about the initial ultimate claims estimates. But for both, much guidance can be drawn from the estimates itself and from the run-off data given. Finally, a numerical example shows how the resulting prediction error compares to the one of the CL method.

Suggested Citation

  • Mack, Thomas, 2008. "The Prediction Error of Bornhuetter/Ferguson," ASTIN Bulletin, Cambridge University Press, vol. 38(1), pages 87-103, May.
  • Handle: RePEc:cup:astinb:v:38:y:2008:i:01:p:87-103_01
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    Cited by:

    1. Michel Dacorogna & Alessandro Ferriero & David Krief, 2018. "One-Year Change Methodologies for Fixed-Sum Insurance Contracts," Risks, MDPI, vol. 6(3), pages 1-29, July.
    2. Karthik Sriram & Peng Shi, 2021. "Stochastic loss reserving: A new perspective from a Dirichlet model," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(1), pages 195-230, March.
    3. M. Hiabu & E. Mammen & M. D. Martìnez-Miranda & J. P. Nielsen, 2016. "In-sample forecasting with local linear survival densities," Biometrika, Biometrika Trust, vol. 103(4), pages 843-859.

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