Regression based reserving models and partial information

Mathias Lindholm and Richard Verrall

Insurance: Mathematics and Economics, 2020, vol. 94, issue C, 109-124

Abstract: We consider a class of distribution-free regression models only defined in terms of moments, which can be used to model separate reported but not settled reserves, and incurred but not reported reserves. These regression models can be estimated using standard least squares and method of moments techniques, similar to those used in the distribution-free chain-ladder model. Further, these regression models are closely related to double chain-ladder type models, and the suggested estimation techniques could serve as alternative estimation procedures for these models. Due to the simple structure of the models it is possible to obtain Mack-type mean squared error of prediction estimators. Moreover, the analysed regression models can be used on different levels of detailed data, and by using the least squares estimation techniques it is possible to show that the precision in the reserve predictor is improved by using more detailed data. These regression models can be seen as a sequence of linear models, and are therefore also easy to bootstrap non-parametrically.

Keywords: Distribution-free linear models; Generalised least squares; Estimation error; Prediction error; Partial information (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:94:y:2020:i:c:p:109-124

DOI: 10.1016/j.insmatheco.2020.07.001

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