[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/p/bam/wpaper/bafes11.html
   My bibliography  Save this paper

A Dual Early Warning Model of Bank Distress

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://repec.bmth.ac.uk/bam/wp/BAFES11.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Sinkey, Joseph F, Jr, 1975. "A Multivariate Statistical Analysis of the Characteristics of Problem Banks," Journal of Finance, American Finance Association, vol. 30(1), pages 21-36, March.
    2. Meyer, Paul A & Pifer, Howard W, 1970. "Prediction of Bank Failures," Journal of Finance, American Finance Association, vol. 25(4), pages 853-868, September.
    3. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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).
    4. 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.
    5. 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.
    6. 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.
    7. Fiordelisi, Franco & Minnucci, Federica & Previati, Daniele & Ricci, Ornella, 2020. "Bail-in regulation and stock market reaction," Economics Letters, Elsevier, vol. 186(C).
    8. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Papanikolaou, Nikolaos I., 2018. "To be bailed out or to be left to fail? A dynamic competing risks hazard analysis," Journal of Financial Stability, Elsevier, vol. 34(C), pages 61-85.
    2. Fiordelisi, Franco & Mare, Davide Salvatore, 2013. "Probability of default and efficiency in cooperative banking," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 30-45.
    3. Mare, Davide Salvatore, 2015. "Contribution of macroeconomic factors to the prediction of small bank failures," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 39(C), pages 25-39.
    4. Gogas, Periklis & Papadimitriou, Theophilos & Agrapetidou, Anna, 2018. "Forecasting bank failures and stress testing: A machine learning approach," International Journal of Forecasting, Elsevier, vol. 34(3), pages 440-455.
    5. Daniel Porath, 2006. "Estimating probabilities of default for German savings banks and credit cooperatives," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 58(3), pages 214-233, July.
    6. Paola Bongini & Stijn Claessens & Giovanni Ferri, 2001. "The Political Economy of Distress in East Asian Financial Institutions," Journal of Financial Services Research, Springer;Western Finance Association, vol. 19(1), pages 5-25, February.
    7. Kick, Thomas & Koetter, Michael, 2007. "Slippery slopes of stress: Ordered failure events in German banking," Journal of Financial Stability, Elsevier, vol. 3(2), pages 132-148, July.
    8. Thomas B. King & Daniel A. Nuxoll & Timothy J. Yeager, 2006. "Are the causes of bank distress changing? can researchers keep up?," Review, Federal Reserve Bank of St. Louis, vol. 88(Jan), pages 57-80.
    9. Halil Erdal & Aykut Ekinci, 2013. "A Comparison of Various Artificial Intelligence Methods in the Prediction of Bank Failures," Computational Economics, Springer;Society for Computational Economics, vol. 42(2), pages 199-215, August.
    10. Kolari, James & Glennon, Dennis & Shin, Hwan & Caputo, Michele, 2002. "Predicting large US commercial bank failures," Journal of Economics and Business, Elsevier, vol. 54(4), pages 361-387.
    11. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
    12. Pavlos Almanidis & Robin C. Sickles, 2016. "Banking Crises, Early Warning Models, and Efficiency," International Series in Operations Research & Management Science, in: Juan Aparicio & C. A. Knox Lovell & Jesus T. Pastor (ed.), Advances in Efficiency and Productivity, chapter 0, pages 331-364, Springer.
    13. Parnes, Dror & Gormus, Alper, 2024. "Prescreening bank failures with K-means clustering: Pros and cons," International Review of Financial Analysis, Elsevier, vol. 93(C).
    14. Stephen M. Miller & Athanasios Noulas, 1995. "Explaining Recent Connecticut Bank Failures," Working papers 1995-01, University of Connecticut, Department of Economics.
    15. Kimmel, Randall K. & Thornton, John H. & Bennett, Sara E., 2016. "Can statistics-based early warning systems detect problem banks before markets?," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 190-216.
    16. Slawomir Juszczyk & Rafal Balina, 2013. "Effectiveness of Polish and Foreign Disdcriminant Models," Diversity, Technology, and Innovation for Operational Competitiveness: Proceedings of the 2013 International Conference on Technology Innovation and Industrial Management,, ToKnowPress.
    17. Colvin, Christopher L. & de Jong, Abe & Fliers, Philip T., 2015. "Predicting the past: Understanding the causes of bank distress in the Netherlands in the 1920s," Explorations in Economic History, Elsevier, vol. 55(C), pages 97-121.
    18. 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.
    19. Lin, Shu Ling & Penm, Jack H.W. & Gong, Shang-Chi & Chang, Ching-Shan, 2005. "Risk-based capital adequacy in assessing on insolvency-risk and financial performances in Taiwan's banking industry," Research in International Business and Finance, Elsevier, vol. 19(1), pages 111-153, March.
    20. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.

    More about this item

    Keywords

    financial crisis; bank distress; early warning model; forecasting power;
    All these keywords.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bam:wpaper:bafes11. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Marta Disegna (email available below). General contact details of provider: https://edirc.repec.org/data/bsbouuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.