Reinsurance and CAT bonds are two alternative risk management instruments used by insurance companies. Insurers should be indifferent between the two instruments in a perfect capital market. However, the theoretical literature suggests that insured risk characteristics and market imperfections may influence the effectiveness and efficiency of reinsurance relative to CAT bonds. CAT bonds may add value to insurers’ risk management strategies and may therefore substitute for reinsurance. Our study is the first to empirically analyse if and under what circumstances CAT bonds can substitute for traditional reinsurance. Our analysis of a comprehensive data set comprising U.S. P&C insurers’ financial statements and CAT bond use shows that insurance companies’ choice of risk management instruments is not arbitrary. We find that the added value of CAT bonds mainly stems from non-indemnity bonds and reveal that (non-indemnity) CAT bonds are valuable under high reinsurer default risk, low basis risk and in high-risk layers.

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Enhanced machine learning methods provide an encouraging alternative to forecast asset prices by extending or generalizing the possible model specifications compared to conventional linear regression methods. Even if enhanced methods of machine learning in the literature often lead to better forecasting quality, this is not clear for small asset classes, because in small asset classes enhanced machine learning methods may potentially over-fit the in-sample data. Against this background, we compare the forecasting performance of linear regression models and enhanced machine learning methods in the market for catastrophe (CAT) bonds. We use linear regression with variable selection, penalization methods, random forests and neural networks to forecast CAT bond premia. Among the considered models, random forests exhibit the highest forecasting performance, followed by linear regression models and neural networks.