A random forest based approach for predicting spreads in the primary catastrophe bond market
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.
Keywords
Cite
@article{arxiv.2001.10393,
title = {A random forest based approach for predicting spreads in the primary catastrophe bond market},
author = {Despoina Makariou and Pauline Barrieu and Yining Chen},
journal= {arXiv preprint arXiv:2001.10393},
year = {2020}
}
Comments
34 pages, 8 figures, 7 tables