IDEAS home Printed from https://ideas.repec.org/a/wly/ijfiec/v22y2017i1p44-67.html

Banking and Currency Crises: Differential Diagnostics for Developed Countries

Author

Listed:
  • Mark Joy
  • Marek Rusnák
  • Kateřina Šmídková
  • Bořek Vašíček

Abstract

We identify a set of ‘rules of thumb’ that characterize economic, financial and structural conditions preceding the onset of banking and currency crises in 36 advanced economies over 1970–2010. We use the classification and regression tree methodology and its random forest extension, which permits the detection of key variables driving binary crisis outcomes, allows for interactions among key variables and determines critical tipping points. We distinguish between basic country conditions, country structural characteristics and international developments. We find that crises are more varied than they are similar. For banking crises, we find that low net interest rate spreads in the banking sector and a shallow, or inverted, yield curve is their most important forerunners in the short term. In the longer term, it is high house price inflation. For currency crises, high domestic short‐term rates coupled with overvalued exchange rates are the most powerful short‐term predictors. We find that both country structural characteristics and international developments are relevant banking‐crisis predictors. Currency crises, however, seem to be driven more by country idiosyncratic, short‐term developments. We find that some variables, such as the domestic credit gap, provide important unconditional signals, but it is difficult to use them as conditional signals and, more importantly, to find relevant threshold values. Copyright © 2016 John Wiley & Sons, Ltd.

Suggested Citation

  • Mark Joy & Marek Rusnák & Kateřina Šmídková & Bořek Vašíček, 2017. "Banking and Currency Crises: Differential Diagnostics for Developed Countries," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 22(1), pages 44-67, January.
  • Handle: RePEc:wly:ijfiec:v:22:y:2017:i:1:p:44-67
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

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


    Cited by:

    1. Da Wang & YingXue Zhou, 2024. "An innovative machine learning workflow to research China’s systemic financial crisis with SHAP value and Shapley regression," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-40, December.
    2. Bjarni G. Einarsson & Kristófer Gunnlaugsson & Thorvardur Tjörvi Ólafsson & Thórarinn G. Pétursson, 2015. "The long history of financial boom-bust cycles in Iceland - Part I: Financial crises," Economics wp68, Department of Economics, Central bank of Iceland.
    3. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
    4. Mr. Daniel Leigh & Weicheng Lian & Mr. Marcos Poplawski Ribeiro & Rachel Szymanski & Viktor Tsyrennikov & Hong Yang, 2017. "Exchange Rates and Trade: A Disconnect?," IMF Working Papers 2017/058, International Monetary Fund.
    5. Vaclav Broz & Dominika Kolcunova & Lukas Pfeifer, 2018. "Risk-Sensitive Capital Regulation," Occasional Publications - Edited Volumes, Czech National Bank, Research and Statistics Department, edition 1, volume 16, number rb16/1 edited by Simona Malovana & Jan Frait, July-Dece.
    6. Gunther Tichy, 2020. "Zur Prognostizierbarkeit von Krisen," WIFO Monatsberichte (monthly reports), WIFO, vol. 93(3), pages 193-206, March.
    7. Levieuge, Grégory & Lucotte, Yannick & Pradines-Jobet, Florian, 2021. "The cost of banking crises: Does the policy framework matter?," Journal of International Money and Finance, Elsevier, vol. 110(C).
    8. Marcin Pietrzak, 2021. "Can Financial Soundness Indicators Help Predict Financial Sector Distress?," IMF Working Papers 2021/197, International Monetary Fund.
    9. Antulov-Fantulin, Nino & Lagravinese, Raffaele & Resce, Giuliano, 2021. "Predicting bankruptcy of local government: A machine learning approach," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 681-699.
    10. Balaga Mohana Rao & Puja Padhi, 2020. "Common Determinants of the Likelihood of Currency Crises in BRICS," Global Business Review, International Management Institute, vol. 21(3), pages 698-712, June.
    11. Stéphanie Pamies Sumner & Katia Berti, 2017. "A Complementary Tool to Monitor Fiscal Stress in European Economies," European Economy - Discussion Papers 049, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    12. Tang, Pan & Tang, Tiantian & Lu, Chennuo, 2024. "Predicting systemic financial risk with interpretable machine learning," The North American Journal of Economics and Finance, Elsevier, vol. 71(C).
    13. Emile du Plessis & Ulrich Fritsche, 2025. "New forecasting methods for an old problem: Predicting 147 years of systemic financial crises," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(1), pages 3-40, January.
    14. Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2023. "Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach," Journal of International Economics, Elsevier, vol. 145(C).
    15. Alexandr Patalaha & Maria A. Shchepeleva, 2023. "Bank Crisis Management Policies and the New Instability," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 6, pages 43-60, December.
    16. Michal Franta & Tibor Hledik & Jan Vlcek & Michal Dvorak & Zlatuse Komarkova & Adam Kucera & Vaclav Broz & Michal Hlavacek, 2018. "Interest Rates," Occasional Publications - Edited Volumes, Czech National Bank, Research and Statistics Department, edition 2, volume 16, number rb16/2 edited by Jan Babecky & Volha Audzei, July-Dece.
    17. Audit, Dooneshsingh & Alam, Nafis, 2022. "Why have credit variables taken centre stage in predicting systemic banking crises?," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 3(1).
    18. Paola Bongini & Małgorzata Iwanicz-Drozdowska & Paweł Smaga & Bartosz Witkowski, 2018. "In search of a measure of banking sector distress: empirical study of CESEE banking sectors," Risk Management, Palgrave Macmillan, vol. 20(3), pages 242-257, August.
    19. Emile du Plessis, 2025. "Can Text-Based Statistical Models Reveal Impending Banking Crises?," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1265-1298, March.
    20. Lanbiao Liu & Chen Chen & Bo Wang, 2022. "Predicting financial crises with machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 871-910, August.
    21. du Plessis, Emile, 2022. "Multinomial modeling methods: Predicting four decades of international banking crises," Economic Systems, Elsevier, vol. 46(2).
    22. Iwanicz-Drozdowska Małgorzata & Kurowski Łukasz, 2021. "Keep your friends close and your enemies closer – the case of monetary policy and financial imbalances," German Economic Review, De Gruyter, vol. 22(4), pages 383-414, November.
    23. Bitetto, Alessandro & Cerchiello, Paola & Mertzanis, Charilaos, 2023. "Measuring financial soundness around the world: A machine learning approach," International Review of Financial Analysis, Elsevier, vol. 85(C).

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications
    • G01 - Financial Economics - - General - - - Financial Crises

    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:wly:ijfiec:v:22:y:2017:i:1:p:44-67. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1076-9307/ .

    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.