Increasing countries’ financial resilience through global catastrophe risk pooling

Extreme weather events such as tropical cyclones, floods, and heavy precipitation can deteriorate macroeconomic performance (e.g., growth, tax revenues, inflation, exchange rates), often forcing governments—especially in low- and middle-income countries—into costly deficit financing and increased debt. Recovery frequently depends on ex-post foreign aid, which is slow and uncertain, whereas ex-ante instruments (e.g., insurance) provide faster, predictable funding and can incentivize risk reduction and preparedness. International agendas (Sendai Framework, Paris Agreement) promote ex-ante risk financing, and sovereign catastrophe risk pools (CCRIF, ARC, PCRAFI) have emerged as promising tools. However, current pools provide mainly first-response coverage, can be undercapitalized by members, sometimes rely on donor-paid premiums, and—critically—were not designed to maximize risk diversification and operate only regionally. This study introduces a method to construct optimal pools that maximize risk diversification with the fewest countries and evaluates how global pooling compares with regional pooling in improving financial resilience, focusing on tropical cyclone risk across four regions and on existing pools CCRIF and PCRAFI.

Study regions and risk focus: Four World Bank regions with tropical cyclone exposure and middle- to low-income status are analyzed: East Asia & Pacific (EAP, 26 countries), Latin America & Caribbean (LAC, 38), South Asia (SA, 7), and Sub-Saharan Africa (SSA, 16). Risk diversification is assessed at the 200-year event (α = 0.995) using Value-at-Risk (VaR), Expected Shortfall (ES, a coherent tail-risk measure), and Marginal Expected Shortfall (MES). Correlations refer to Pearson correlations of annual total losses exceeding the 200-year loss.

Risk diversification metric and optimization: Risk diversification (RD) is defined as 1 minus risk concentration (RC), where RC reflects the capital required for the pooled portfolio relative to individual risks, focusing on tail losses beyond VaR. For multiple pools, a vector x assigns each country to one pool (or none). RD_j(x,j) = 1 − RC(x,j). Step 1 solves a many-objective optimization to minimize RC_j(x,j) for m pools, yielding sets of countries that minimize each pool’s concentration. Step 2 solves, for each pool, a single-objective optimization to find the smallest subset (binary vector z_j) that achieves the minimal RC_j found in Step 1, thus identifying pools with maximum diversification using the fewest countries. Optimization is performed with Pymoo: GA for single-objective and U-NSGA-III for many-objective problems; seed analysis with 15 runs, retaining the non-dominated union of solutions. Convergence is documented in Supplementary Figs. S2–S7.

Hazard event set and simulation of years: Because historical records are insufficient for 200-year ES estimation, a global synthetic tropical cyclone track dataset (>90,000 events for 1979–2019) is generated using Emanuel’s statistical–dynamical downscaling approach based on ECMWF ERA5 reanalysis. Tropical cyclones are seeded stochastically, advected via a beta-and-advection model, and intensities simulated with CHIPS. Event frequency is calibrated to observations. A 10,000-year series is created by classifying ENSO-like year types (persistent warm/cold vs. neutral) within 1979–2019, sampling a 10,000-year sequence via a multinomial distribution, and then sampling years and storm counts (Poisson with mean annual frequency) and events accordingly to assemble each synthetic year.

Impact modeling (CLIMADA): Tropical cyclone wind fields are computed using a parametric Holland wind model on each synthetic track at 300 arc-sec (~10 km) resolution. Exposure is modeled via LitPop, allocating asset values proportional to nightlight intensity and population. Vulnerability functions for tropical cyclones are those calibrated by Eberenz et al. Damages (annual total losses) are computed per country for the 10,000-year series. These underpin ES, MES, and bilateral correlations used in optimization.

Pooling scenarios: (i) Regional optimal pools: For each region independently, solve the first optimization step to derive a single optimal pool per region (max RD) and quantify composition and diversification. (ii) Globally diversified regional pools: Extend the four regional optimal pools simultaneously by allowing additions from any region (countries not already in their own region’s optimal pool), solving a four-objective problem to find Pareto-optimal global extensions. (iii) Existing pools: Apply the method to PCRAFI (EAP focus) and CCRIF (LAC focus). Assess current diversification, optimal regional enhancements, and Pareto-optimal global extensions, including country risk-share distributions in each configuration.

Software, data, and parameters: Optimization uses Pymoo (GA and U-NSGA-III). Synthetic TC data from WindRiskTech are fed into CLIMADA; derived impact data and code are available via Zenodo (DOI: 10.5281/zenodo.7371742). Key evaluation threshold: 200-year return period (α = 0.995).

• Optimal regional pools: Diversification (RD) is highest in LAC (0.75), followed by EAP (0.66), SSA (0.50), and SA (0.33). Pool composition favors countries with low mutual correlations and/or low marginal risk shares. Highly correlated neighbors (e.g., AIA–BLM; MAF–SXM in LAC; VNM–KHM in EAP; BTN–BGD in SA; ZWE–ZAF, SOM–ETH in SSA) are typically excluded. Some moderately correlated countries are included when their individual risk share in the pool is small (e.g., BRB–LCA in LAC; WSM–ASM in EAP).
• Global pooling of optimal regional pools: All four regions experience Pareto improvements in diversification when allowed to pool globally. Gains are largest where regional RD was lowest: SA doubles from ~0.34 to 0.70; SSA rises from 0.50 to 0.70. EAP increases from 0.66 to ~0.75; LAC from 0.75 to ~0.80. Trade-offs arise because certain countries are jointly desirable for multiple regions (e.g., MYS, VNM, CUB, DOM, JAM, MEX, PAN, TZA). Global pooling generally reduces all countries’ risk shares by redistributing pooled risk across regions and can even enable inclusion of previously excluded correlated countries at very low shares (e.g., SXM 0.09 and TCA 0.03 in LAC despite correlation ~0.35).
• Existing pools (PCRAFI and CCRIF): Current diversification is moderate (PCRAFI RD 0.49; CCRIF RD 0.48) with high concentration in a few members (PNG near 1.0 in PCRAFI; JAM 0.94 in CCRIF). Optimal regional pooling increases RD to 0.66 for PCRAFI (+35%) and to 0.67 for CCRIF (+~40%). For CCRIF, this remains 11% short of the regional maximum (0.75 in LAC), indicating initial design limitations due to loss concentration in JAM.
• Global optimal pooling yields larger gains than regional: PCRAFI can reach RD 0.81 (+65% from 0.49); CCRIF can reach RD 0.77 (+60% from 0.48). Trade-offs between PCRAFI and CCRIF are minor (CCRIF configurations 0.75–0.77). In globally diversified PCRAFI (EAP), top risk shares shift to cross-regional members with low correlation: COL 0.59, CRI 0.33 (LAC), MUS 0.31 (SSA). In globally diversified CCRIF (LAC), top shares come from EAP and SA: MYS 0.42, VNM 0.50 (EAP), BGD 0.43 (SA). PNG and JAM see substantial reductions in their shares with both regional and global pooling, with global pooling avoiding increases in other regional members’ shares.
• Overall: Global pooling consistently enhances diversification, spreads risk more evenly, and increases the number of countries that can benefit from pooling. Optimal global pooling could increase diversification in existing pools by up to 65%.

The study demonstrates that optimizing pool composition for diversification and expanding beyond regional boundaries can materially enhance the financial efficiency of sovereign catastrophe risk pools. By lowering capital requirements through improved diversification, global pooling can reduce premiums for given coverage or increase coverage for given premiums, thereby strengthening fiscal resilience after disasters. However, globally optimal expansions entail trade-offs: some countries are pivotal to maximizing multiple pools simultaneously, so no single global configuration jointly maximizes all pools’ diversification. Implementation therefore requires political judgment and coordination among prospective members to select feasible groupings that balance benefits across pools. For existing pools, regional optimization can mitigate concentration risk but may be insufficient when loss profiles are dominated by single countries (e.g., CCRIF’s JAM). Global pooling offers larger, more uniform reductions in member risk shares and improves the inclusivity of pooling by enabling additional (even moderately correlated) countries to participate effectively with small shares. Nevertheless, enhanced diversification and pool reconfiguration alone do not fully resolve the broader challenge that current sovereign pools typically fund only first-response needs, leaving countries dependent on aid for full recovery. Complementary reforms in coverage levels, capital structure, and integration with broader risk management strategies remain necessary.

This work introduces a general optimization framework to form sovereign catastrophe risk pools that maximize risk diversification with the fewest members and applies it to tropical cyclone risk across four regions and two existing pools (PCRAFI, CCRIF). Results show that: (i) optimal regional pooling improves diversification; (ii) optimal global pooling consistently yields larger gains, redistributes risk more evenly, and broadens participation; and (iii) existing pools could raise diversification substantially—up to 65% for PCRAFI and 60% for CCRIF—by adopting optimized global expansions. These improvements imply lower capital needs and the potential for lower premiums or higher coverage, enhancing financial resilience. Future research should extend the approach to other hazards and multi-hazard settings, evaluate the impacts of increased diversification on (re)insurance program design, and assess optimal pool compositions under evolving socio-economic and climate conditions. Practical implementation will require addressing governance, coordination, and equity considerations among participating countries.

• Hazard scope: The analysis focuses exclusively on tropical cyclone risk; results should not be generalized to other perils without dedicated modeling.
• Data and modeling: Losses are derived from synthetic track sets and CLIMADA-based impact modeling (wind model, exposure via LitPop, regional vulnerability functions), which involve assumptions and calibration that may affect tail loss estimates and correlations.
• Objective and metric choices: Diversification is assessed at the 200-year level (α = 0.995) using ES-based formulations; alternative thresholds or risk measures could yield different pool compositions.
• Trade-offs and implementability: Pareto-optimal global configurations entail competition for the same countries; achieving the theoretically optimal grouping depends on political feasibility, governance, and countries’ willingness to cooperate.
• Coverage adequacy: Even with improved diversification, current sovereign pools provide mainly first-response funding; increased diversification alone does not ensure sufficient resources for full recovery.
• Regional membership constraints: Some findings reflect current regional/income classifications and available country sets; changes in eligibility or membership could alter optimal configurations.