A Bayesian valuation framework for catastrophe bonds
针对巨灾债券市场不完整导致的定价不确定性，提出一个基于贝叶斯方法的估值框架，量化巨灾和利率的不确定性，用于估计不同风险特征巨灾债券的公允价格和预期风险溢价。
Abstract Catastrophe (CAT) bond markets are incomplete and hence carry uncertainty in instrument pricing. Various pricing approaches have been proposed, but none treats the uncertainty in catastrophes and interest rates in a sufficiently flexible and statistically reliable way within an asset valuation framework. Consequently, little is known empirically about the expected risk premium of CAT bonds. The primary contribution of this article is to present a Bayesian CAT bond valuation framework based on uncertainty quantification of catastrophes and interest rates. We leverage this framework to estimate fair value prices and expected risk premiums for CAT bonds with varying catastrophe risk profiles.