Emerging Signals

This paper presents a two-tier modelling framework for estimating twelve-month sovereign Eurobond default probabilities (PDs) in emerging and frontier markets. The framework combines a baseline logistic regression model (Tier 1) with a Light Gradient Boosted Machine (LightGBM) residual correction layer (Tier 2). The first tier provides a stable and interpretable mapping from fundamental macro-financial covariates to early-warning PD estimates. The second tier then addresses systematic misspecification by capturing residual variation through flexible functional forms and non-linear interactions, while accommodating a wider set of country-specific variables. This two-tier design thus preserves transparency and explainability at its core, while extending predictive accuracy through modern machine learning techniques.