Geopolitics, Geoeconomics and Risk: A Machine Learning Approach
Alvaro Ortiz, Tomasa Rodrigo, Pablo Saborido
TL;DR
The paper builds a high-frequency daily panel for 42 countries, marrying sovereign CDS spreads with news-based indicators of geopolitics and policy uncertainty to study their impact on sovereign risk. It conducts a comprehensive machine-learning horse race, showing that incorporating news improves forecast accuracy—especially with nonlinear models like ensemble trees—while enabling interpretable narratives via SHAP values and interaction terms. The results reveal a hierarchical transmission: global financial conditions anchor risk, domestic macro sentiment and policy uncertainty amplify effects nonlinearly, and geopolitical shocks trigger episodic co-movements with regional heterogeneity. Case studies of Russia–Ukraine, Hamas–Israel, and U.S. tariff episodes illustrate distinct transmission channels and underscore the need to couple high-frequency news signals with global financial dynamics for effective sovereign-risk surveillance.
Abstract
We introduce a novel high-frequency daily panel dataset of both markets and news-based indicators -- including Geopolitical Risk, Economic Policy Uncertainty, Trade Policy Uncertainty, and Political Sentiment -- for 42 countries across both emerging and developed markets. Using this dataset, we study how sentiment dynamics shape sovereign risk, measured by Credit Default Swap (CDS) spreads, and evaluate their forecasting value relative to traditional drivers such as global monetary policy and market volatility. Our horse-race analysis of forecasting models demonstrates that incorporating news-based indicators significantly enhances predictive accuracy and enriches the analysis, with non-linear machine learning methods -- particularly Random Forests -- delivering the largest gains. Our analysis reveals that while global financial variables remain the dominant drivers of sovereign risk, geopolitical risk and economic policy uncertainty also play a meaningful role. Crucially, their effects are amplified through non-linear interactions with global financial conditions. Finally, we document pronounced regional heterogeneity, as certain asset classes and emerging markets exhibit heightened sensitivity to shocks in policy rates, global financial volatility, and geopolitical risk.