Chain ladder method: Bayesian bootstrap versus classical bootstrap

Gareth W. Peters*, Mario V. Wüthrich, Pavel V. Shevchenko

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


The intention of this paper is to estimate a Bayesian distribution-free chain ladder (DFCL) model using approximate Bayesian computation (ABC) methodology. We demonstrate how to estimate quantities of interest in claims reserving and compare the estimates to those obtained from classical and credibility approaches. In this context, a novel numerical procedure utilizing a Markov chain Monte Carlo (MCMC) technique, ABC and a Bayesian bootstrap procedure was developed in a truly distribution-free setting. The ABC methodology arises because we work in a distribution-free setting in which we make no parametric assumptions, meaning we cannot evaluate the likelihood point-wise or in this case simulate directly from the likelihood model. The use of a bootstrap procedure allows us to generate samples from the intractable likelihood without the requirement of distributional assumptions; this is crucial to the ABC framework. The developed methodology is used to obtain the empirical distribution of the DFCL model parameters and the predictive distribution of the outstanding loss liabilities conditional on the observed claims. We then estimate predictive Bayesian capital estimates, the value at risk (VaR) and the mean square error of prediction (MSEP). The latter is compared with the classical bootstrap and credibility methods.

Original languageEnglish
Pages (from-to)36-51
Number of pages16
JournalInsurance: Mathematics and Economics
Issue number1
Publication statusPublished - Aug 2010
Externally publishedYes


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