Advancing sustainable financing through pandemic bond: a Bayesian model averaging approach to liquidity premia.
- 1Department of Global Health, School of Public Health, Peking University, No.38, Xueyuan Road, Haidian District, Beijing, 100191 China.
- 2Institute for Global Health and Development, Peking University, Beijing, 100871 China.
Abstract
Pandemic bonds, as a subtype of catastrophe (CAT) bonds, are designed to transfer public health risks to capital markets, yet they face substantial liquidity risk due to their nascent market structure and limited risk design. Using a relatively small sample from 2020 to 2024, we apply Bayesian model averaging (BMA) and interpretable machine learning (IML) to examine the associations of macroeconomic and bond-specific characteristics with liquidity premia across market phases. We find that the average liquidity premium of natural catastrophe bonds (1364 bps) is substantially higher than that of pandemic bonds (452 bps), potentially reflecting differences in bond characteristics and contractual structures. The estimates suggest that higher ESG scores and higher disclosed health scores are associated with lower estimated pandemic-bond premia, with health-score categories receiving slightly greater posterior inclusion support than ESG in the joint specification. Pandemic bond premia are higher during hard market phases, while several predictors receive greater posterior inclusion support during soft markets. Overall, the results suggest that incorporating a health-specific index, alongside insights from traditional CAT and green bond design, could enhance the resilience of pandemic bonds and strengthen their role in sustainable development. The online version contains supplementary material available at https://doi.org/10.1186/s40854-026-00991-0.
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BibTeX
@article{yao2026advancing,
title = {{Advancing sustainable financing through pandemic bond: a Bayesian model averaging approach to liquidity premia.}},
author = {Tianen Yao and Xinyu Yang and Yuhang Pan and Zhihan Cui and Ming Xu},
journal = {Financial innovation},
year = {2026},
month = nov,
doi = {10.1186/s40854-026-00991-0},
url = {https://europepmc.org/article/MED/42999455},
note = {CC BY},
}
RIS
TY - JOUR
TI - Advancing sustainable financing through pandemic bond: a Bayesian model averaging approach to liquidity premia.
AU - Tianen Yao
AU - Xinyu Yang
AU - Yuhang Pan
AU - Zhihan Cui
AU - Ming Xu
PY - 2026
DA - 2026/11/06
JO - Financial innovation
DO - 10.1186/s40854-026-00991-0
UR - https://europepmc.org/article/MED/42999455
AB - Pandemic bonds, as a subtype of catastrophe (CAT) bonds, are designed to transfer public health risks to capital markets, yet they face substantial liquidity risk due to their nascent market structure and limited risk design. Using a relatively small sample from 2020 to 2024, we apply Bayesian model averaging (BMA) and interpretable machine learning (IML) to examine the associations of macroeconomic and bond-specific characteristics with liquidity premia across market phases. We find that the average liquidity premium of natural catastrophe bonds (1364 bps) is substantially higher than that of pandemic bonds (452 bps), potentially reflecting differences in bond characteristics and contractual structures. The estimates suggest that higher ESG scores and higher disclosed health scores are associated with lower estimated pandemic-bond premia, with health-score categories receiving slightly greater posterior inclusion support than ESG in the joint specification. Pandemic bond premia are higher during hard market phases, while several predictors receive greater posterior inclusion support during soft markets. Overall, the results suggest that incorporating a health-specific index, alongside insights from traditional CAT and green bond design, could enhance the resilience of pandemic bonds and strengthen their role in sustainable development. The online version contains supplementary material available at https://doi.org/10.1186/s40854-026-00991-0.
SN - 2199-4730
ER -