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Forecasting sovereign risk in the Euro area via machine learning

Author

Listed:
  • Guillaume Belly

    (Banque de France - Banque de France - Banque de France)

  • Lukas Boeckelmann

    (ECB, Frankfurt)

  • Carlos Mateo Caicedo Graciano

    (Banque de France - Banque de France - Banque de France)

  • Alberto Di Iorio

    (BI - Banca d´Italia)

  • Klodiana Istrefi

    (Banque de France - Banque de France - Banque de France, CEPR - Center for Economic Policy Research)

  • Vasileios Siakoulis

    (Bank of Greece)

  • Arthur Stalla-Bourdillon

    (Banque de France - Banque de France - Banque de France, Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres, DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

Abstract

We test the usefulness of machine learning (ML) for the valuation and pricing of sovereign risk in the Euro area along two important dimensions: i) its predictive accuracy compared with traditional econometric methods, and ii) its assessment of the main economic factors underlying market perceptions of sovereign risk.We find that ML techniques can capture the dynamics inherent in the market valuation of country risk far more efficiently than traditional econometric models, both in the cross‐section and in the time series. Moreover, we show that public sentiment about financial news, redenomination fears and the degree of hawkishness/dovishness expressed in the ECB president's speeches are major contributors to sovereign bond spreads. We also confirm that macroeconomic and global financial factors affect sovereign risk assessment and the corresponding formation of sovereign spreads.

Suggested Citation

  • Guillaume Belly & Lukas Boeckelmann & Carlos Mateo Caicedo Graciano & Alberto Di Iorio & Klodiana Istrefi & Vasileios Siakoulis & Arthur Stalla-Bourdillon, 2023. "Forecasting sovereign risk in the Euro area via machine learning," Post-Print hal-04459577, HAL.
    • Handle: RePEc:hal:journl:hal-04459577
    DOI: 10.1002/for.2938
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    Cited by:

    1. Noah Cheruiyot Mutai & Karim Farag & Lawrence Ibeh & Kaddour Chelabi & Nguyen Manh Cuong & Olufunke Mercy Popoola, 2025. "AI Driven Fiscal Risk Assessment in the Eurozone: A Machine Learning Approach to Public Debt Vulnerability," FinTech, MDPI, vol. 4(3), pages 1-14, June.
    2. Bolivar, Osmar, 2024. "GDP nowcasting: A machine learning and remote sensing data-based approach for Bolivia," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 5(3).
    3. Sourov Ahmed & Marjan Akter Badhon & Mahmudul Hassan Maruf, 2025. "A Survey-Driven Ensemble Approach to Predicting Sovereign Debt Distress in Bangladesh," International Journal of Scientific Research and Modern Technology, Prasu Publications, vol. 4(10), pages 103-114.
    4. Amelie BARBIER-GAUCHARD & Emmanouil SOFIANOS, 2024. "Forecasting Public Debt in the Euro Area Using Machine Learning: Decision Tools for Financial Markets," Working Papers of BETA 2024-47, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.

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