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A Dual Early Warning Model of Bank Distress

Author

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  • Nikolaos I. Papanikolaou

    (Bournemouth University)

Abstract

We contribute to the better understanding of the key factors related to the operation of the banking system that led to the global financial crisis through the development of a dual earning warning model that explores the joint determination of the probability of a distressed bank to face a licence withdrawal or to be bailed out. The underlying patterns of distress are analysed based upon a wide spectrum of bank-specific and environmental factors. We obtain precise parameter estimates and superior in- and out-of-sample forecasts. Our results show that the determinants of failures and those of bailouts differ to a considerable extent, revealing that authorities treat a distressed bank differently in their decision to let it fail or to bail it out. Overall, we provide a reliable mechanism for preventing welfare losses due to bank distress.

Suggested Citation

  • Nikolaos I. Papanikolaou, 2017. "A Dual Early Warning Model of Bank Distress," BAFES Working Papers BAFES11, Department of Accounting, Finance & Economic, Bournemouth University.
    • Handle: RePEc:bam:wpaper:bafes11
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    File URL: https://repec.bmth.ac.uk/bam/wp/BAFES11.pdf
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    Cited by:

    1. Norfaizah Othman & Mariani Abdul-Majid & Aisyah Abdul-Rahman, 2018. "Determinants of Banking Crises in ASEAN Countries," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., vol. 9(03), pages 1-20, October.
    2. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    3. Evžen Kočenda & Ichiro Iwasaki, 2022. "Bank survival around the world: A meta‐analytic review," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 108-156, February.
    4. Fiordelisi, Franco & Minnucci, Federica & Previati, Daniele & Ricci, Ornella, 2020. "Bail-in regulation and stock market reaction," Economics Letters, Elsevier, vol. 186(C).
    5. Grodecka-Messi, Anna & Kenny, Seán & Ögren, Anders, 2021. "Predictors of bank distress: The 1907 crisis in Sweden," Explorations in Economic History, Elsevier, vol. 80(C).
    6. Elena G. Shershneva, 2024. "CAMELS parameters’ impact on the risk of losing financial stability: The case of Russian banks," Journal of New Economy, Ural State University of Economics, vol. 25(2), pages 130-152, July.
    7. Elena G. Shershneva, Min Zhou Hao, 2024. "Russian Banks Financial Stability Loss Diagnostic: Multidimensional Logit-Model Approach," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 23(2), pages 476-498.
    8. Irfan Nurfalah & Aam Slamet Rusydiana & Nisful Laila & Eko Fajar Cahyono, 2018. "Early Warning to Banking Crises in the Dual Financial System in Indonesia: The Markov Switching Approach التحذير المبكر من الأزمات المصرفية في النظام المالي المزدوج في إندونيسيا: مقاربة ماركوف للتحويل," Journal of King Abdulaziz University: Islamic Economics, King Abdulaziz University, Islamic Economics Institute., vol. 31(2), pages 133-156, July.

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    Keywords

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    JEL classification:

    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G01 - Financial Economics - - General - - - Financial Crises
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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