We investigate the heterogeneous effects of market timing decisions using a causal machine learning approach. Our analysis explores how issuance timing affects catastrophe bond spreads, identifying key drivers of variability. The findings suggest that early-year issuances typically lead to lower spreads, with the magnitude of this effect varying according to market conditions and bond-specific characteristics. Understanding these timing dynamics is essential for optimizing the cost of capital and ensuring the success of catastrophe bond offerings for sponsors and investors.
Despoina Makariou (University of St. Gallen)