Measuring fragmentation risk in European bond markets using Machine Learning

Roland Bouillot, Bertrand Candelon and Clemens Kool
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Bertrand Candelon: Université catholique de Louvain, LIDAM/LFIN, Belgium
Clemens Kool: Maastricht University

No 2026006, LIDAM Reprints LFIN from Université catholique de Louvain, Louvain Finance (LFIN)

Abstract: This paper asks whether machine learning can forecast euro area sovereign bond spreads and whether the resulting forecasts can track financial fragmentation. Using a new high-dimensional monthly dataset of 4,948 macro-financial series for ten euro area countries from December 2008 to February 2025, we run a horse race among thirteen machine-learning models and two simple benchmarks, an AR(1) process and a random walk. XGBoost is the strongest machine-learning model and is never significantly outperformed by the other learners. It is not, however, the most accurate one-month-ahead forecaster: under strict out-of-sample re-estimation the AR(1) and the random walk attain lower point-forecast errors in every country. The value of the machine-learning approach lies elsewhere. SHAP decompositions recover the macrofinancial drivers of the predicted spreads. The forecasts form the basis of a fragmentation indicator built by clustering predicted spread paths. The indicator reproduces the core-periphery divide in the windows running to 2022. French spreads then decouple from the core after 2023. By 2024 to 2025 France and Belgium form a distinct cluster, a new source of fragmentation risk with direct implications for the transmission of a single monetary policy.

Keywords: Machine learning; Financial fragmentation risk; XGBoost; Sovereign spreads (search for similar items in EconPapers)
Date: 2026-09-01
Note: In: Annals of Operation Research, 2026
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