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Approximate Bayesian Computations to fit and compare insurance loss models

Author

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  • Pierre-Olivier Goffard

    (UCBL - Université Claude Bernard Lyon 1 - Université de Lyon, ISFA - Institut de Science Financière et d'Assurances, LSAF - Laboratoire de Sciences Actuarielles et Financières [Lyon] - ISFA - Institut de Science Financière et d'Assurances)

  • Patrick Laub

    (University of Melbourne, ISFA - Institut de Science Financière et d'Assurances)

Abstract

Approximate Bayesian Computation (ABC) is a statistical learning technique to calibrate and select models by comparing observed data to simulated data. This technique bypasses the use of the likelihood and requires only the ability to generate synthetic data from the models of interest. We apply ABC to fit and compare insurance loss models using aggregated data. A state-of-the-art ABC implementation in Python is proposed. It uses sequential Monte Carlo to sample from the posterior distribution and the Wasserstein distance to compare the observed and synthetic data. MSC 2010 : 60G55, 60G40, 12E10.

Suggested Citation

  • Pierre-Olivier Goffard & Patrick Laub, 2021. "Approximate Bayesian Computations to fit and compare insurance loss models," Post-Print hal-02891046, HAL.
    • Handle: RePEc:hal:journl:hal-02891046
    DOI: 10.1016/j.insmatheco.2021.06.002
    Note: View the original document on HAL open archive server: https://hal.science/hal-02891046v2
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    References listed on IDEAS

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