Forecasting European Sovereign Spreads using Machine Learning
(2025) , 53 pages
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- Abstract
- Accurate forecasting constitutes a central objective for policymakers. This paper examines the application of advanced machine-learning techniques to predict the 10-year sovereign bond spreads vis-à-vis the German Bund, employing a novel high-dimensional dataset covering 10 European countries over the period 2008 − 2025. An exhaustive comparison of predictive performance, both in-sample and out-of-sample, demonstrates that XGBoost delivers the highest degree of accuracy. Building on these forecast-based spreads, we construct fragmentation matrices that capture the extent of asymmetry across euro area sovereign bond markets. Prior to the COVID-19 crisis, results confirm the well-documented clustering between core and peripheral countries. However, since 2023 this segmentation appears to have weakened, as French and Belgian spreads exhibit a synchronous trajectory. These findings contribute to the literature on financial integration and fragmentation within the euro area, offering new insights into the evolving dynamics of sovereign bond markets.
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Citations
Bouillot, R., Candelon, B., & Kool, C. (2025). Forecasting European Sovereign Spreads using Machine Learning (LIDAM Discussion Paper LFIN 2025/04). https://hdl.handle.net/2078.5/272666