Pricing Pandemic Bonds under Hull–White & Stochastic Logistic Growth Model

Vajira Manathunga () and Linmiao Deng
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Vajira Manathunga: Program of Actuarial Science, Department of Mathematical Sciences, College of Basic and Applied Sciences, Middle Tennessee State University, Murfreesboro, TN 37132, USA
Linmiao Deng: Program of Actuarial Science, Department of Mathematical Sciences, College of Basic and Applied Sciences, Middle Tennessee State University, Murfreesboro, TN 37132, USA

Risks, 2023, vol. 11, issue 9, 1-28

Abstract: Pandemic bonds can be used as an effective tool to mitigate the economic losses that governments face during pandemics and transfer them to the global capital market. Once considered as an “uninsurable” event, pandemic bonds caught the attention of the world with the issuance of pandemic bonds by the World Bank in 2017. Compared to other CAT bonds, pandemic bonds received less attention from actuaries, industry professionals, and academic researchers. Existing research focused mainly on how to bring epidemiological parameters to the pricing mechanism through compartmental models. In this study, we introduce the stochastic logistic growth model-based pandemic bond pricing framework. We demonstrate the proposed model with two numerical examples. First, we calculate what investor is willing to pay for the World Bank issued pandemic bond while accounting for possible future pandemic, but require to have the same yield to maturity when no pandemic is there, and without using COVID-19 data. In the second example, we calculate the fair value of a pandemic bond with characteristics similar to the World Bank issued pandemic bond, but using COVID-19 data. The model can be used as an alternative to epidemic compartmental model-based pandemic bond pricing mechanisms.

Keywords: pandemic bond; stochastic logistic growth model; CAT bond; Hull–White model; Weibull distribution; trigger events; World Bank; COVID-19; epidemic modeling (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2023
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