Speaker: Pierre-O Goffard 
 

Title : Sequential Monte Carlo samplers to fit and compare insurance loss models

Abstract: Insurance loss distributions are characterized by a high frequency of small amounts and a lower, but not insignificant, occurrence of large claim amounts. Composite models, which link two probability distributions, one for the “belly” and the other for the “tail” of the loss distribution, have emerged in the actuarial literature to take this specificity into account. The parameters of these models summarize the distribution of the losses. One of them corresponds to the breaking point between small and large claim amounts. The composite models are usually fitted using maximum likelihood estimation. A Bayesian approach is considered in this work. Sequential Monte Carlo samplers are used to sample from the posterior distribution and compute the posterior model evidences to both fit and compare the competing models. The method is validated via a simulation study and illustrated on an insurance loss dataset.

The paper is available at https://hal.archives-ouvertes.fr/hal-03263471/

Bio: Pierre-O Goffard is an associate professor at l'Institut de science financière et d’assurances, a graduate school specializing in actuarial science and part of Universite Lyon 1. His research interests focus on probabilities and statistics applied to risk management of insurance companies and optimization of blockchain systems. He was a visiting assistant professor in the PSTAT department at UCSB from 2016 to 2018, he is currently visiting the department until the end of the spring quarter.