Pricing Catastrophe Bonds -- A Probabilistic Machine Learning Approach

arXiv:2405.00697v1 Announce Type: cross
Abstract: This paper proposes a probabilistic machine learning method to price catastrophe (CAT) bonds in the primary market. The proposed method combines machine-learning-based predictive models with Conformal Prediction, an innovative algorithm that generates distribution-free probabilistic forecasts for CAT bond prices. Using primary market CAT bond transaction records between January 1999 and March 2021, the proposed method is found to be more robust and yields more accurate predictions of the bond spreads than traditional regression-based methods. Furthermore, …

abstract algorithm arxiv bond bonds catastrophe cs.lg distribution free machine machine learning market paper prediction predictive predictive models price pricing q-fin.cp q-fin.pr records stat.ap type