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Rules of Thumb for Banking Crises in Emerging Markets

Author

Listed:
  • P. Manasse
  • R. Savona
  • M. Vezzoli
Abstract
This paper employs a recent statistical algorithm (CRAGGING) in order to build an early warning model for banking crises in emerging markets. We perturb our data set many times and create artificial samples from which we estimated our model, so that, by construction, it is flexible enough to be applied to new data for out-of-sample prediction. We find that, out of a large number (540) of candidate explanatory variables, from macroeconomic to balance sheet indicators of the countries financial sector, we can accurately predict banking crises by just a handful of variables. Using data over the period from 1980 to 2010, the model identifies two basic types of banking crises in emerging markets: a Latin American type , resulting from the combination of a (past) credit boom, a flight from domestic assets, and high levels of interest rates on deposits; and an Asian type , which is characterized by an investment boom financed by banks foreign debt. We compare our model to other models obtained using more traditional techniques, a Stepwise Logit, a Classification Tree, and an Average model, and we find that our model strongly dominates the others in terms of out-of-sample predictive power.

Suggested Citation

  • P. Manasse & R. Savona & M. Vezzoli, 2013. "Rules of Thumb for Banking Crises in Emerging Markets," Working Papers wp872, Dipartimento Scienze Economiche, Universita' di Bologna.
    • Handle: RePEc:bol:bodewp:wp872
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    References listed on IDEAS

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    2. Detken, Carsten & Weeken, Olaf & Alessi, Lucia & Bonfim, Diana & Boucinha, Miguel & Castro, Christian & Frontczak, Sebastian & Giordana, Gaston & Giese, Julia & Wildmann, Nadya & Kakes, Jan & Klaus, B, 2014. "Operationalising the countercyclical capital buffer: indicator selection, threshold identification and calibration options," ESRB Occasional Paper Series 5, European Systemic Risk Board.
    3. Ons Jedidi & Jean-Sébastien Pentecôte, 2015. "Prédire les crises bancaires : un système d’alerte robuste," Revue française d'économie, Presses de Sciences-Po, vol. 0(3), pages 189-225.
    4. Alessi, Lucia & Detken, Carsten, 2018. "Identifying excessive credit growth and leverage," Journal of Financial Stability, Elsevier, vol. 35(C), pages 215-225.
    5. Ons Jedidi & Jean Sébastien Pentecote, 2015. "Robust Signals for Banking Crises," Economics Bulletin, AccessEcon, vol. 35(3), pages 1617-1629.
    6. Waelchli Boris, 2016. "A proximity based macro stress testing framework," Dependence Modeling, De Gruyter, vol. 4(1), pages 1-26, November.
    7. Mikhail Stolbov, 2015. "Anatomy of international banking crises at the onset of the Great Recession," International Economics and Economic Policy, Springer, vol. 12(4), pages 553-569, October.
    8. Mr. Plamen K Iossifov, 2021. "Cyclical Patterns of Systemic Risk Metrics: Cross-Country Analysis," IMF Working Papers 2021/028, International Monetary Fund.
    9. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
    10. Carsten Detken & Olaf Weeken & Lucia Alessi & Diana Bonfim & Miguel M. Boucinha & Christian Castro & Sebastian Frontczak & Gaston Giordana & Julia Giese & Nadya Jahn & Jan Kakes & Benjamin Klaus & Jan, 2014. "Operationalising the countercyclical capital buffer: indicator selection, threshold identification and calibration options," ESRB Occasional Paper Series 05, European Systemic Risk Board.

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    More about this item

    JEL classification:

    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G01 - Financial Economics - - General - - - Financial Crises
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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