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Stochastic Claims Reserving Via A Bayesian Spline Model With Random Loss Ratio Effects

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  • Gao, Guangyuan
  • Meng, Shengwang

Abstract

We propose a Bayesian spline model which uses a natural cubic B-spline basis with knots placed at every development period to estimate the unpaid claims. Analogous to the smoothing parameter in a smoothing spline, shrinkage priors are assumed for the coefficients of basis functions. The accident period effect is modeled as a random effect, which facilitate the prediction in a new accident period. For model inference, we use Stan to implement the no-U-turn sampler, an automatically tuned Hamiltonian Monte Carlo. The proposed model is applied to the workers' compensation insurance data in the United States. The lower triangle data is used to validate the model.

Suggested Citation

  • Gao, Guangyuan & Meng, Shengwang, 2018. "Stochastic Claims Reserving Via A Bayesian Spline Model With Random Loss Ratio Effects," ASTIN Bulletin, Cambridge University Press, vol. 48(1), pages 55-88, January.
    • Handle: RePEc:cup:astinb:v:48:y:2018:i:01:p:55-88_00
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    Cited by:

    1. R. H. Ilyasov & V. A. Plotnikov, 2022. "Oil Production and Carbon Emissions: Spline Analysis of Relationships," Administrative Consulting, Russian Presidential Academy of National Economy and Public Administration. North-West Institute of Management., issue 5.
    2. Greg Taylor, 2019. "Loss Reserving Models: Granular and Machine Learning Forms," Risks, MDPI, vol. 7(3), pages 1-18, July.
    3. Venter Gary, 2019. "Regularized Regression for Reserving and Mortality Models," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 13(2), pages 1-10, July.

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