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The use of machine learning algorithms, specifically XGBoost in this paper, and the subsequent application of model interpretability techniques of SHAP and LIME significantly improved the predictive and explanatory power of the credit risk models developed in the paper. Sovereign credit risk is a function of not just the macroeconomic environment of a country, but also the state of its private sector and its exposure to sustainability risk factors.
Sovereign debt has traditionally been regarded a safe asset class. However, the picture has shifted in the last decade, with a significant increase in the number of sovereign defaults. The drivers of this increase are complex and as yet unexplored, but it is clear that factors other than traditional macroeconomic events are significantly underpinning these economies.
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