Approximate Bayesian Computations to fit and compare insurance loss models

Pierre-Olivier Goffard () and Patrick Laub ()
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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 Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon
Patrick Laub: University of Melbourne, ISFA - Institut de Science Financière et d'Assurances

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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.

Keywords: Bayesian statistics; approximate Bayesian computation; likelihood- free inference; risk management (search for similar items in EconPapers)
Date: 2021-09
Note: View the original document on HAL open archive server: https://hal.science/hal-02891046v2
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Citations: View citations in EconPapers (1)

Published in Insurance: Mathematics and Economics, 2021, 100, pp.350-371. ⟨10.1016/j.insmatheco.2021.06.002⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02891046

DOI: 10.1016/j.insmatheco.2021.06.002

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