Forecasting European Sovereign Spreads using Machine Learning

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 2007−2025. 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 forecasts, 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 2021 this segmentation appears to have weakened, as French and Belgian spreads exhibit a synchronous trajectory. Thesefindingscontribute totheliterature on financialintegrationand fragmentation within the Euro area, offering new insights into the evolving dynamics of sovereign bond markets.