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Predicting European bank stress tests: Survival of the fittest

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  • Kolari, James W.
  • López-Iturriaga, Félix J.
  • Sanz, Ivan Pastor
Abstract
This paper tests the hypothesis that stress tests are primarily a function of the fundamental financial condition and operating environment of individual banks, rather than alternative adverse economic and financial scenarios imposed by regulators. We develop a novel early warning system based on multiple strategy ensemble methods to predict whether European banks pass stress tests in 2010, 2011 and 2014. The model is able to identify over 98% of failing and passing banks in the training subsample and predict about 90% of banks in the test validation sample. Further analyses of predictor importance and robustness compared to other competing model approaches are conducted. Our evidence supports the conclusion that, regardless of different macroeconomic scenarios, surviving stress tests depends largely on the underlying risk dimensions of individual banks.

Suggested Citation

  • Kolari, James W. & López-Iturriaga, Félix J. & Sanz, Ivan Pastor, 2019. "Predicting European bank stress tests: Survival of the fittest," Global Finance Journal, Elsevier, vol. 39(C), pages 44-57.
    • Handle: RePEc:eee:glofin:v:39:y:2019:i:c:p:44-57
    DOI: 10.1016/j.gfj.2018.01.015
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    4. Małgorzata Iwanicz-Drozdowska & Krzysztof Jackowicz & Maciej Karczmarczyk, 2021. "“The Crooked Smile of TCR†: Banks’ Solvency and Restructuring Costs in the European Banking Industry," SAGE Open, , vol. 11(3), pages 21582440211, September.
    5. Kolari, James W. & López-Iturriaga, Félix J. & Sanz, Ivan Pastor, 2020. "Measuring systemic risk in the U.S. Banking system," Economic Modelling, Elsevier, vol. 91(C), pages 646-658.
    6. Cristina Gutiérrez-López & Julio Abad-González, 2020. "Sustainability in the Banking Sector: A Predictive Model for the European Banking Union in the Aftermath of the Financial Crisis," Sustainability, MDPI, vol. 12(6), pages 1-25, March.
    7. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
    8. Li Xian Liu & Shuangzhe Liu & Milind Sathye, 2021. "Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research," JRFM, MDPI, vol. 14(10), pages 1-24, October.
    9. Kristóf, Tamás & Virág, Miklós, 2022. "EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks," Research in International Business and Finance, Elsevier, vol. 61(C).
    10. Pedro Guerra & Mauro Castelli & Nadine Côrte-Real, 2022. "Approaching European Supervisory Risk Assessment with SupTech: A Proposal of an Early Warning System," Risks, MDPI, vol. 10(4), pages 1-23, March.
    11. Iwanicz-Drozdowska, Małgorzata & Witkowski, Bartosz, 2022. "Regulation and supervision of the European banking industry. Does one size fit all?," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 113-129.

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    More about this item

    Keywords

    Banking; Early warning system; Financial condition; Systemic risk; Stress test;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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