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

2020/01/28 by Makariou, Despoina, Barrieu, Pauline, Chen, Yining · 1 citation
#FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (stat.ML) #Pricing of Securities (q-fin.PR)

paper · doi:10.48550/arxiv.2001.10393

Abstract

We introduce a random forest approach to enable spreads' prediction in the primary catastrophe bond market. We investigate whether all information provided to investors in the offering circular prior to a new issuance is equally important in predicting its spread. The whole population of non-life catastrophe bonds issued from December 2009 to May 2018 is used. The random forest shows an impressive predictive power on unseen primary catastrophe bond data explaining 93% of the total variability. For comparison, linear regression, our benchmark model, has inferior predictive performance explaining only 47% of the total variability. All details provided in the offering circular are predictive of spread but in a varying degree. The stability of the results is studied. The usage of random forest can speed up investment decisions in the catastrophe bond industry.

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