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Uncertainty in heteroscedastic Bayesian model averaging

Author

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  • Jessup, Sébastien
  • Mailhot, Mélina
  • Pigeon, Mathieu

Abstract

The literature concerning liability evaluation is very well developed. It is however almost exclusively devoted to the performance of singular models. Recently, a variant of Bayesian Model Averaging (BMA) has been used for the first time to combine outstanding claims models. BMA is a widely used tool for model combination using Bayesian inference. Different versions of an expectation-maximisation (EM) algorithm are frequently used to apply BMA. This algorithm however has the issue of convergence to a single model. In this paper, we propose a numerical error integration approach to address the problem of convergence in a heteroscedastic context. We also generalise the proposed error integration approach by considering weights as a Dirichlet random variable, allowing for weights to vary. We compare the proposed approaches through simulation studies and a Property & Casualty insurance simulated dataset. We discuss some advantages of the proposed methods.

Suggested Citation

  • Jessup, Sébastien & Mailhot, Mélina & Pigeon, Mathieu, 2025. "Uncertainty in heteroscedastic Bayesian model averaging," Insurance: Mathematics and Economics, Elsevier, vol. 121(C), pages 63-78.
    • Handle: RePEc:eee:insuma:v:121:y:2025:i:c:p:63-78
    DOI: 10.1016/j.insmatheco.2024.12.008
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