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Banking and Currency Crises: Differential Diagnostics for Developed Countries

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  • 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.

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  • 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
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    2. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
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    4. Vaclav Broz & Dominika Kolcunova & Simona Malovana & Lukas Pfeifer, 2018. "Risk-Sensitive Capital Regulation," Occasional Publications - Edited Volumes, Czech National Bank, edition 1, volume 16, number rb16/1 edited by Simona Malovana & Jan Frait, January.
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    7. Marcin Pietrzak, 2021. "Can Financial Soundness Indicators Help Predict Financial Sector Distress?," IMF Working Papers 2021/197, International Monetary Fund.
    8. Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kang, Miao & Kapadia, Sujit & Simsek, Özgür, 2020. "Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach," Bank of England working papers 848, Bank of England.
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    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. 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.
    13. 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, edition 2, volume 16, number rb16/2 edited by Jan Babecky & Volha Audzei, January.
    14. 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).
    15. 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.
    16. 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.
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    18. 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.
    19. 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).

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    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

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