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.

翻译：我们构建了一个新颖的高频日度面板数据集，涵盖42个新兴市场与发达国家的市场指标及新闻文本指标——包括地缘政治风险、经济政策不确定性、贸易政策不确定性与政治情绪。基于该数据集，我们研究了情绪动态如何影响以信用违约互换（CDS）利差衡量的主权风险，并评估其相对于全球货币政策与市场波动等传统驱动因素的预测价值。通过预测模型的"赛马"比较分析，我们发现引入新闻文本指标能显著提升预测精度并丰富分析维度，其中非线性机器学习方法——特别是随机森林——带来的改进最为显著。分析表明，尽管全球金融变量仍是主权风险的主要驱动因素，地缘政治风险与经济政策不确定性同样具有重要影响。关键在于，这些因素通过与全球金融状况的非线性交互作用产生放大效应。最后，我们观察到显著的地区异质性：特定资产类别与新兴市场对政策利率冲击、全球金融波动及地缘政治风险表现出更高的敏感性。