Measuring fragmentation risk in European bond markets using Machine Learning
(2026) Annals of Operations Research — (2026)
Files
LFIN_RP_2026-06.pdf
Embargo Access until 2027-09-01 - Adobe PDF
- 3.34 MB
Details
- Authors
- 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.
- Affiliations
Citations
Bouillot, R., Candelon, B., & Kool, C. (2026). Measuring fragmentation risk in European bond markets using Machine Learning. Annals of Operations Research. Accepted/in-press. https://hdl.handle.net/2078.5/280050 (Original work published 2026)