Sovereign Default Probability Stack

Problem solved

Investors and policy lenders need a probability-of-default estimate that decomposes into actionable factors: external buffers, debt service profile, fiscal anchor, banking sector contingent liability, political durability, market access. Standard sovereign ratings are slow and binary; CDS is volatile and thin in many EM names. SDPS produces a 12-month and 36-month default probability with a named factor decomposition and a peer-distance view.

Inputs

• IMF Article IV consultation reports and program reviews
• Central bank reserve adequacy versus IMF ARA metric (gross reserves, short-term external debt, broad money, imports)
• Debt stock and amortization schedule (IMF WEO, World Bank IDS, BIS, Refinitiv, Bloomberg)
• Fiscal balance and primary surplus path (IMF Fiscal Monitor, country-MoF data)
• Banking sector NPL and capitalization (FSAP, central bank financial stability reports, ECB SSM where applicable)
• Political tenure and constitutional constraint data (V-Dem, Polity5, executive-tenure dataset)
• Sovereign CDS and EMBI Global spreads (J.P. Morgan EMBI, Bloomberg)
• Recent program participation history (IMF program archive, Paris Club minutes, Common Framework status)

Outputs

• 12-month default probability
• 36-month default probability
• Factor decomposition table (six factors, contribution to probability)
• Distance-to-default to nearest peer with peer cluster identification
• Scenario probability under three external shocks (oil price, FX shock, global rates)
• Common Framework feasibility view if default occurs (creditor mix, China bilateral share)
• Replication package with named source links and rebuild scripts

Method

1. Step 1. Pull each of the six factors: external buffers, debt service, fiscal anchor, banking sector, political durability, market access.
2. Step 2. Standardize each factor to z-scores against the post-Common Framework EM panel (post-2020).
3. Step 3. Apply factor weights calibrated by logistic regression against 2010 to 2025 default and pre-emptive-restructuring events.
4. Step 4. Compute 12-month and 36-month default probabilities from the logistic output.
5. Step 5. Run sensitivity to three external shocks (oil 30 percent move, FX 20 percent depreciation, US rates 100 bps move).
6. Step 6. Output peer comparison: nearest peer by Mahalanobis distance, peer-cluster average probability.
7. Step 7. Run Common Framework feasibility check: creditor-mix split (multilateral, bilateral, commercial), China bilateral share, holdout litigation exposure.
8. Step 8. Output replication package with named citations and rebuild scripts.

Assumptions

• Logistic calibration uses 35-plus EM episodes from 2010 to 2025 (hard defaults plus pre-emptive restructurings).
• Reserve adequacy follows IMF ARA composite; users can re-run at gross reserves only.
• Banking sector NPLs are reported at face value; FSAP-flagged underreporting is noted but not adjudicated.
• Political tenure proxies use executive-tenure dataset and V-Dem; users can substitute country-specific tenure data.
• Common Framework participation outcomes are scenario-based, not predicted.

Limitations

• Pre-emptive vs. hard default distinction is structural, not modeled in the base probability.
• China bilateral debt position is partial in many EM names; the framework uses Horn-Reinhart-Trebesch and AidData estimates with explicit confidence ranges.
• Political tenure proxies are imperfect for personalist regimes and for parliamentary systems with frequent reshuffles.
• EMBI and CDS data is thin for the smallest EM names; the framework reports a wider band when market data is sparse.
• Three-shock scenario set is illustrative, not exhaustive; users can substitute country-specific shock vectors.

Example application

Applied to the Pakistan, Egypt, Argentina cluster 2026: Pakistan scores highest 12-month probability under base case (binding external buffers, thin reserve adequacy, political durability flagged), Egypt scores second (debt service profile dominates, fiscal anchor improving post-program), and Argentina scores lowest 12-month but highest 36-month (Milei stabilization is the swing variable). The peer-distance view places Pakistan nearest to Sri Lanka pre-2022 by Mahalanobis distance, with the Common Framework feasibility check turning on China bilateral participation. See Pakistan in 2026: IMF program economics under fiscal stress.