Advancing sustainable financing through pandemic bond: a Bayesian model averaging approach to liquidity premia

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.