论文标题
一种基于森林的随机方法，用于预测主要灾难债券市场的利差
A random forest based approach for predicting spreads in the primary catastrophe bond market
论文作者
论文摘要
我们引入了一种随机的森林方法，以实现在主要灾难债券市场中的差异预测。我们调查在新发行之前向投资者提供的所有信息在预测其传播方面同样重要。使用了从2009年12月至2018年5月发行的全部非生活灾难债券的全部人口。随机森林对看不见的原发性灾难键数据具有令人印象深刻的预测能力，解释了总变异性的93％。为了进行比较，我们的基准模型线性回归具有较低的预测性能，仅解释了总变异性的47％。发行通告中提供的所有细节都可以预测扩散，但程度不同。研究了结果的稳定性。随机森林的使用可以加快灾难债券行业的投资决策。
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.
