A random forest based approach for predicting spreads in the primary catastrophe bond market

Despoina Makariou, Pauline Barrieu and Yining Chen

Insurance: Mathematics and Economics, 2021, vol. 101, issue PB, 140-162

Abstract: We introduce a random forest approach to enable spreads' prediction in the primary catastrophe bond market. In a purely predictive framework, we assess the importance of catastrophe spread predictors using permutation and minimal depth methods. The whole population of non-life catastrophe bonds issued from December 2009 to May 2018 is used. We find that random forest has at least as good prediction performance as our benchmark-linear regression in the temporal context, and better prediction performance in the non-temporal one. Random forest also performs better than the benchmark when multiple predictors are excluded in accordance with the importance rankings or at random, which indicates that random forest extracts information from existing predictors more effectively and captures interactions better without the need to specify them. The results of random forest, in terms of prediction accuracy and the minimal depth importance are stable. There is only a small divergence between the drivers of catastrophe bond spread in the predictive versus explanatory framework. We believe that the usage of random forest can speed up investment decisions in the catastrophe bond industry both for would-be issuers and investors.

Keywords: Catastrophe bond pricing; Interactions; Machine learning in insurance; Minimal depth importance; Permutation importance; Primary market spread prediction; Random forest; Stability (search for similar items in EconPapers)
JEL-codes: G1 G22 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:101:y:2021:i:pb:p:140-162

DOI: 10.1016/j.insmatheco.2021.07.003

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