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Are emerging market currency crises predictable? A test

Author

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  • Peltonen, Tuomas A.

Abstract

This paper analyzes the predictability of emerging market currency crises by comparing the often used probit model to a new method, namely a multi-layer perceptron artificial neural network (ANN) model. According to the results, both models were able to signal currency crises reasonably well in-sample, but the forecasting power of these models out-ofsample was found to be rather poor. Only in the case of Russian (1998) crisis were both models able to signal the crisis well in advance. The results reinforced the view that developing a stable model that can predict or even explain currency crises is a challenging task. JEL Classification: F31, E44, C25, C23, C45

Suggested Citation

  • Peltonen, Tuomas A., 2006. "Are emerging market currency crises predictable? A test," Working Paper Series 571, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:2006571
    Note: 355041
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    File URL: https://www.ecb.europa.eu//pub/pdf/scpwps/ecbwp571.pdf
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    Cited by:

    1. Knedlik, Tobias, 2006. "Signaling Currency Crises in South Africa," IWH Discussion Papers 19/2006, Halle Institute for Economic Research (IWH).
    2. Dany-Knedlik, Geraldine & Kämpfe, Martina & Knedlik, Tobias, 2021. "The appropriateness of the macroeconomic imbalance procedure for Central and Eastern European Countries," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, pages 123-139.
    3. Matthew S. Yiu & Alex Ho & Lu Jin, 2009. "Econometric Approach to Early Warnings of Vulnerability in the Banking System and Currency Markets for Hong Kong and Other EMEAP Economies," Working Papers 0908, Hong Kong Monetary Authority.
    4. Alexis CRUZ-RODRIGUEZ, 2016. "Exchange Arrangements and Currency Crises: What´s the matter with the exchange rate classification?," Journal of Economics and Political Economy, KSP Journals, vol. 3(2), pages 377-392, June.
    5. Markus Holopainen & Peter Sarlin, 2017. "Toward robust early-warning models: a horse race, ensembles and model uncertainty," Quantitative Finance, Taylor & Francis Journals, vol. 17(12), pages 1933-1963, December.
    6. Sarlin, Peter & Peltonen, Tuomas A., 2013. "Mapping the state of financial stability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 46-76.
    7. Robalo Marques, Carlos & Dias, Daniel & Santos Silva, João M. C., 2006. "Measuring the importance of the uniform nonsynchronization hypothesis," Working Paper Series 606, European Central Bank.
    8. Peter Sarlin & Dorina Marghescu, 2011. "Neuro‐Genetic Predictions Of Currency Crises," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(4), pages 145-160, October.
    9. Neziri, Hekuran, 2008. "Can Credit Default Swaps Predict Financial Crises: An Empirical Test on Emerging Markets," MPRA Paper 13096, University Library of Munich, Germany.
    10. Dorina Marghescu & Peter Sarlin & Shuhua Liu, 2010. "Early‐warning analysis for currency crises in emerging markets: A revisit with fuzzy clustering," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 17(3‐4), pages 143-165, July.
    11. Jesús Crespo Cuaresma & Tomáš Slacík, 2008. "Determinants of Currency Crises: A Conflict of Generations?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 1, pages 126-141.
    12. Vesna Bucevska, 2011. "Growth effect of aid and its volatility: An individual country study in South Asian economies," Business and Economic Horizons (BEH), Prague Development Center, vol. 4(1), pages 13-26, January.
    13. John Adams & Ali Metwally, 2019. "Identifying currency crises indicators: the case of Egypt," African Journal of Economic and Management Studies, Emerald Group Publishing, vol. 10(2), pages 241-259, June.
    14. Crespo Cuaresma, Jesús & Slacik, Tomas, 2009. "On the determinants of currency crises: The role of model uncertainty," Journal of Macroeconomics, Elsevier, vol. 31(4), pages 621-632, December.
    15. Sarlin, Peter & Peltonen, Tuomas A., 2013. "Mapping the state of financial stability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 46-76.
    16. Dungey, Mardi & Islam, Raisul & Volkov, Vladimir, 2020. "Crisis transmission: Visualizing vulnerability," Pacific-Basin Finance Journal, Elsevier, vol. 59(C).
    17. Sarlin, Peter & Holopainen, Markus, 2016. "Toward robust early-warning models: a horse race, ensembles and model uncertainty," Working Paper Series 1900, European Central Bank.
    18. Knedlik, Tobias & Scheufele, Rolf, 2007. "Three methods of forecasting currency crises: Which made the run in signaling the South African currency crisis of June 2006?," IWH Discussion Papers 17/2007, Halle Institute for Economic Research (IWH).
    19. Peter Sarlin & Dorina Marghescu, 2011. "Visual predictions of currency crises using self‐organizing maps," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(1), pages 15-38, January.
    20. Markus Holopainen & Peter Sarlin, 2015. "Toward robust early-warning models: A horse race, ensembles and model uncertainty," Papers 1501.04682, arXiv.org, revised Apr 2016.
    21. Aleksandra A. Maslowska, 2011. "Quest for the Best: How to Measure Central Bank Independence and Show its Relationship with Inflation," Czech Economic Review, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, vol. 5(2), pages 132-161, August.

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    More about this item

    Keywords

    artificial neural networks; currency crises; emerging markets;
    All these keywords.

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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