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Machine learning models for predicting catastrophe bond coupons using climate data

Julia Kończal, Michał Balcerek, Krzysztof Burnecki

TL;DR

This paper addresses predicting CAT bond coupons by incorporating large-scale climate variability indicators into pricing models. It compares a Braun 2016 benchmark with an extended climate-augmented specification and evaluates seven machine learning algorithms, including extremely randomized trees. The results show that climate indicators improve both point forecasts (lower RMSE) and probabilistic forecasts (well-calibrated VaR), with extremely randomized trees achieving the strongest gains. The findings support climate-aware pricing of CAT bonds and offer a practical ML approach for investors and issuers to better quantify catastrophe risk.

Abstract

In recent years, the growing frequency and severity of natural disasters have increased the need for effective tools to manage catastrophe risk. Catastrophe (CAT) bonds allow the transfer of part of this risk to investors, offering an alternative to traditional reinsurance. This paper examines the role of climate variability in CAT bond pricing and evaluates the predictive performance of various machine learning models in forecasting CAT bond coupons. We combine features typically used in the literature with a new set of climate indicators, including Oceanic Ni{ñ}o Index, Arctic Oscillation, North Atlantic Oscillation, Outgoing Longwave Radiation, Pacific-North American pattern, Pacific Decadal Oscillation, Southern Oscillation Index, and sea surface temperatures. We compare the performance of linear regression with several machine learning algorithms, such as random forest, gradient boosting, extremely randomized trees, and extreme gradient boosting. Our results show that including climate-related variables improves predictive accuracy across all models, with extremely randomized trees achieving the lowest root mean squared error (RMSE). These findings suggest that large-scale climate variability has a measurable influence on CAT bond pricing and that machine learning methods can effectively capture these complex relationships.

Machine learning models for predicting catastrophe bond coupons using climate data

TL;DR

This paper addresses predicting CAT bond coupons by incorporating large-scale climate variability indicators into pricing models. It compares a Braun 2016 benchmark with an extended climate-augmented specification and evaluates seven machine learning algorithms, including extremely randomized trees. The results show that climate indicators improve both point forecasts (lower RMSE) and probabilistic forecasts (well-calibrated VaR), with extremely randomized trees achieving the strongest gains. The findings support climate-aware pricing of CAT bonds and offer a practical ML approach for investors and issuers to better quantify catastrophe risk.

Abstract

In recent years, the growing frequency and severity of natural disasters have increased the need for effective tools to manage catastrophe risk. Catastrophe (CAT) bonds allow the transfer of part of this risk to investors, offering an alternative to traditional reinsurance. This paper examines the role of climate variability in CAT bond pricing and evaluates the predictive performance of various machine learning models in forecasting CAT bond coupons. We combine features typically used in the literature with a new set of climate indicators, including Oceanic Ni{ñ}o Index, Arctic Oscillation, North Atlantic Oscillation, Outgoing Longwave Radiation, Pacific-North American pattern, Pacific Decadal Oscillation, Southern Oscillation Index, and sea surface temperatures. We compare the performance of linear regression with several machine learning algorithms, such as random forest, gradient boosting, extremely randomized trees, and extreme gradient boosting. Our results show that including climate-related variables improves predictive accuracy across all models, with extremely randomized trees achieving the lowest root mean squared error (RMSE). These findings suggest that large-scale climate variability has a measurable influence on CAT bond pricing and that machine learning methods can effectively capture these complex relationships.
Paper Structure (6 sections, 24 equations, 3 figures, 3 tables)

This paper contains 6 sections, 24 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Data
  3. Climate variability variables
  4. Linear regression comparison
  5. Machine learning approach
  6. Conclusions

Figures (3)

  • Figure 1: Geographical distribution of bond issuance across countries. The map presents the number of tranches issued in the primary market between June 1997 and December 2020.
  • Figure 2: Correlation between catastrophe bond coupon rates and lagged values of large-scale climate indices. The x-axis shows the number of months each climate index is shifted backward to capture delayed effects, while the y-axis presents the corresponding correlation coefficient.
  • Figure 3: Correlation between catastrophe bond coupon rates and lagged SST across various oceanic regions. The $x$--axis represents the number of months by which SST values are shifted backward, and the $y$--axis shows the corresponding correlation coefficient.