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A multivariate evolutionary generalised linear model framework with adaptive estimation for claims reserving

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  • Avanzi, Benjamin
  • Taylor, Greg
  • Vu, Phuong Anh
  • Wong, Bernard

Abstract

In this paper, we develop a multivariate evolutionary generalised linear model (GLM) framework for claims reserving, which allows for dynamic features of claims activity in conjunction with dependency across business lines to accurately assess claims reserves. We extend the traditional GLM reserving framework on two fronts: GLM fixed factors are allowed to evolve in a recursive manner, and dependence is incorporated in the specification of these factors using a common shock approach.

Suggested Citation

  • Avanzi, Benjamin & Taylor, Greg & Vu, Phuong Anh & Wong, Bernard, 2020. "A multivariate evolutionary generalised linear model framework with adaptive estimation for claims reserving," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 50-71.
    • Handle: RePEc:eee:insuma:v:93:y:2020:i:c:p:50-71
    DOI: 10.1016/j.insmatheco.2020.04.007
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    References listed on IDEAS

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    Cited by:

    1. Nataliya Chukhrova & Arne Johannssen, 2021. "Kalman Filter Learning Algorithms and State Space Representations for Stochastic Claims Reserving," Risks, MDPI, vol. 9(6), pages 1-5, June.
    2. Yanez, Juan Sebastian & Pigeon, Mathieu, 2021. "Micro-level parametric duration-frequency-severity modeling for outstanding claim payments," Insurance: Mathematics and Economics, Elsevier, vol. 98(C), pages 106-119.

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    More about this item

    Keywords

    Claims reserving; Evolutionary GLM; Adaptive reserving; Particle learning; Common shock models;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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