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Approximate Bayesian Computations to fit and compare insurance loss models

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  • Goffard, Pierre-Olivier
  • Laub, Patrick J.

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.

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  • Goffard, Pierre-Olivier & Laub, Patrick J., 2021. "Approximate Bayesian Computations to fit and compare insurance loss models," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 350-371.
    • Handle: RePEc:eee:insuma:v:100:y:2021:i:c:p:350-371
    DOI: 10.1016/j.insmatheco.2021.06.002
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    2. Giulia Livieri & Davide Radi & Elia Smaniotto, 2023. "Pricing Transition Risk with a Jump-Diffusion Credit Risk Model: Evidences from the CDS market," Papers 2303.12483, arXiv.org.

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    More about this item

    Keywords

    Bayesian statistics; Approximate Bayesian computation; Likelihood-free inference; Statistical claim modeling; Sequential Monte Carlo; Wasserstein distance; Compound distribution;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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