🤖 AI Summary
This study examines the predictive power of news-derived geopolitical risk (GPR) and economic policy uncertainty (EPU) indices for sovereign credit risk—measured by CDS spreads—and assesses their incremental value over conventional indicators such as VIX and Fed rate expectations. Using daily panel data from 42 countries, we construct a high-frequency, multidimensional GPR–EPU framework integrating news text analytics with nonlinear machine learning models—particularly random forests—to detect non-linear interaction effects and region-specific heterogeneity in risk transmission. Results demonstrate that news-based indicators significantly improve out-of-sample CDS spread forecasting accuracy; random forests consistently outperform traditional econometric models; and the impact of GPR and EPU exhibits state dependence, intensifying markedly during market turmoil. The study provides novel empirical evidence and a methodological framework for modeling unstructured information–driven sovereign risk, advancing both financial economics and macro-financial early-warning systems.
📝 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.