A random forest based approach for predicting spreads in the primary catastrophe bond market

A Unified Bayesian Framework for Pricing Catastrophe Bond Derivatives

Catastrophe (CAT) bond markets are incomplete and hence carry uncertainty in instrument pricing. As such various pricing approaches have been proposed, but none treat the uncertainty in catastrophe occurrences and interest rates in a sufficiently flexible and statistically reliable way within a unifying asset pricing framework. Consequently, little is known empirically about the expected risk-premia of CAT bonds. The primary contribution of this paper is to present a unified Bayesian CAT bond pricing framework based on uncertainty quantification of catastrophes and interest rates. Our framewor…

Valuation of contingent convertible catastrophe bonds - the case for equity conversion

Within the context of the banking-related literature on contingent convertible bonds, we comprehensively formalise the design and features of a relatively new type of insurance-linked security, called a contingent convertible catastrophe bond (CocoCat). We begin with a discussion of its design and compare its relative merits to catastrophe bonds and catastrophe-equity puts. Subsequently, we derive analytical valuation formulae for index-linked CocoCats under the assumption of independence between natural catastrophe and financial markets risks. We model natural catastrophe losses by a time-inh…

多地区视角下的灾害债券建模与估值

Modelling and valuation of catastrophe bonds across multiple regions

本文提出多地区视角下的灾害债券定价模型，通过不同依赖情景分析及正态近似方法，结合Wang变换评估市场风险溢价，以提升灾害债券的估值准确性。

使用神经网络替代模型进行巨灾债券快速估值

Fast catastrophe bond valuation with neural-network surrogates

研究针对巨灾债券结构定价慢的问题，开发神经网络替代模型，通过重要性抽样的蒙特卡罗模拟生成训练标签，实现快速域内结构估值并产生经济可解释的敏感性，提升巨灾债券估值效率。

巨灾债券和其他长期保险型合同的负荷定价

Loading Pricing of Catastrophe Bonds and Other Long-Dated, Insurance-Type Contracts

巨灾风险是个人、公司和整个经济体面临的主要威胁。巨灾（CAT）债券已经成为一种抵消这种风险的方法，相应的文献已经开发出来，试图为这些和其他长期的保险型合同提供一个市场一致的定价方法。本文旨在统一和推广几种广泛使用的长期合约定价方法，重点是程式化的CAT债券和市场一致性估值。它提出了一个装载定价的概念，结合了理论上可能的最低价格的合同，其正式获得的风险中性价格，而不会产生经济上有意义的套利。加载度控制了正式获得的风险中性价格对市场价格的影响程度。一个关键的发现是，这种加载程度必须是恒定的最小波动的合同，是一个重要的，可衡量的长期合同的价格特征。与传统定价方法相比，装载定价允许长期保险型合同的定价更便宜，投资回报率更高。加载定价使保险公司能够系统地积累所需的准备金，以市场一致的方式管理其破产风险。

揭示巨灾债券定价中的非线性动态：基于机器学习的视角

Unveiling Nonlinear Dynamics in Catastrophe Bond Pricing: A Machine Learning Perspective

本文提出利用机器学习模型对巨灾债券进行定价，揭示了传统线性模型忽视的风险因素间非线性关系与复杂交互，基于1999至2021年交易数据验证了该方法能提升定价准确性并深化对风险结构的理解。