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Forecasting Systemic Risk in the European Banking Industry: A Machine Learning Approach

Author

Listed:
  • Zeinab Srour

    (Department of Financial Studies, College of Business Administration, Rafik Hariri University, Block G, Room G101-C, P.O. Box 10, Damour-Chouf 2010, Lebanon)

  • Jamil Hammoud

    (Department of Financial Studies, College of Business Administration, Rafik Hariri University, Block G, Room G101-C, P.O. Box 10, Damour-Chouf 2010, Lebanon)

  • Mohamed Tarabay

    (Department of Financial Studies, College of Business Administration, Rafik Hariri University, Block G, Room G101-C, P.O. Box 10, Damour-Chouf 2010, Lebanon)

Abstract

The aim of this article is to forecast the systemic risk contribution and exposure measured by the delta conditional value at risk (ΔCoVaR) and the marginal expected shortfall (MES), respectively. We first estimate the ΔCoVaR and MES for banks in 16 European countries for the 2002–2016 period. We then predict systemic risk measures using machine learning techniques, such as artificial neural network (ANN) and support vector machine (SVM), and we use AR-GARCH specification. Finally, we compare the methods’ forecasting values and the actual values. Our results show that two hidden layers of artificial neural networks perform efficiently in forecasting systemic risk.

Suggested Citation

  • Zeinab Srour & Jamil Hammoud & Mohamed Tarabay, 2025. "Forecasting Systemic Risk in the European Banking Industry: A Machine Learning Approach," JRFM, MDPI, vol. 18(6), pages 1-20, June.
    • Handle: RePEc:gam:jjrfmx:v:18:y:2025:i:6:p:335-:d:1682304
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