2020 | 42 | 20-47
Article title

Leading indicators of sovereign debt and currency crises: Comparative analysis of 2001 and 2018 shocks in Argentina

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Aim/purpose – This paper investigates the accuracy of leading indicators in the case of the 2001 sovereign default crisis and the 2018 currency turmoil in Argentina.Design/methodology/approach – In this paper, we conducted early warning signals analysis based on a-priori selected variables. For each of the macroeconomic variables, we computed yearly changes and selected the threshold to minimise the noise-to-signal ratio, i.e. the ratio of percentage of false signals in ‘normal’ times to percentage of good signals in a two-year period preceding each of the crises.Findings – The predictive power of indicators differs significantly in various crisis epi-sodes. For the 2001 crisis, the decline in value of bank deposits was the best leading indicator based on the noise-to-signal ratio. For the 2018 currency crisis, the lowest noise-to-signal ratio was observed for the lending-deposit rate ratio.Research implications/limitations – The survey is limited mostly by the data availabil-ity and their quality.Originality/value/contribution – This paper gives a complex review of the major early warning indicators in the context of the most recent history of Argentina’s economy. It applies a set of classical leading indicators to two modern cases of financial crises. The paper proposes an original ‘knocking the window’ approach to the presentation of tradi-tional warning concepts in the context of current economic events.
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  • Faculty of Economic Sciences. University of Warsaw, Poland
  • Faculty of Economic Sciences University of Warsaw, Poland
  • Antunes, A., Bonfim, D., Monteiro, N., & Rodrigues, P. M. M. (2018). Forecasting banking crises with dynamic panel probit models. International Journal of Forecasting, 34(2), 249-275.
  • Argentina’s banks rue the loss of last year’s future. (2018). Retrieved January 31, 2020 from
  • Beutel, J., List, S., & von Schweinitz, G. (2018). An evaluation of early warning models for systemic banking crises: Does machine learning improve predictions? (Discussion Papers 48/2018). Berlin: Deutsche Bundesbank. Retrieved August 14, 2020, from
  • Bluwstein, K., Buckmann, M., Joseph, A., Kang, M., Kapadia, S., & Simsek, O. (2020). Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach (Working Paper, No. 848). London: Bank of England. Retrieved August 14, 2020 from
  • Bräuning, M., Malikkidou, D., Scricco, G., & Scalone, S. (2019). A new approach to Early Warning Systems for small European banks (Working Paper Series, No. 2348). Brussels: European Central Bank. Retrieved August 14, 2020 from
  • Brüggemann, A., & Linne, T. (1999). How good are leading indicators for currency and banking crises in Central and Eastern Europe? An empirical test (IWH Discussion Papers 95). Halle (Saale): Leibniz-Institut für Wirtschaftsforschung Halle (IWH). Retrieved August 14, 2020 from
  • Calvo, G. A. (1995). Varieties of capital-market crises (Working Paper, No. 306). Washington: Inter-American Development Bank. Retrieved August 14, 2020 from
  • Calvo, S., & Reinhart, C. (1996). Capital flows to Latin America: Is there evidence of contagion effects? (Policy Research Working Paper, No. 1619). Washington: World Bank. Retrieved August 14, 2020 from
  • Casabianca, E. J., Catalano, M., Forni, L., Giarda, E., & Passeri, S. (2019). An Early Warning System for banking crises: From regression-based analysis to machine learning techniques (Marco Fanno Working Papers, No. 235). Retrieved August 14, 2020 from
  • Central Bank of the Argentine Republic. (2019). Statistics. Retrieved January 31, 2020 from
  • Chang, R., & Velasco, A. (2001). A model of financial crises in emerging markets. The Quarterly Journal of Economics, 116, 489-517.
  • Danieli, L., & Jakubik, P. (2018). Early warning system for the European Insurance Sector (EIOPA Financial Stability Report – Thematic Articles 13). Frankfurt am Main: Risks and Financial Stability Department, EIOPA. Retrieved August 14, 2020 from
  • Dornbusch, R., Goldfajn, I., & Valdes, R. O. (1995). Currency crises and collapses. Brookings Papers on Economic Activity, 26(2), 219-294. Retrieved August 14, 2020 from
  • Eichengreen, B., & Arteta, C. (2000). Banking crises in emerging markets: presumptions and evidence (Working Paper C00-115). Berkeley: Center for International and Development Economics Research. Retrieved August 14, 2020 from
  • Evans, O., Leone, A. M., Gill, M., & Hilbers, P. (2000). Macroprudential indicators of financial system soundness (Occasional Paper, No. 192). Washington: IMF.
  • Federal Reserve Economic Data. (2019). FRED Economic Data. St. Louis: Federal Reserve Bank. Retrieved January 31, 2020 from
  • Frankel, J. A., & Rose, A. K. (1996). Currency crashes in emerging markets: Empirical indicators (Working Paper, No. 5437). Cambridge, MA: NBER.
  • Gerlach, S., & Smets, F. (1994). Contagious speculative attacks. European Journal of Political Economy, 11, 45-63.
  • Hermansen, M., & Röhn, O. (2017). Economic resilience: The usefulness of early warning indicators in OECD countries. OECD Journal: Economic Studies, 2016(1), 9-35.
  • Holopainen, M., & Sarlin, P. (2017). Toward robust early-warning models: A horse race, ensembles and model uncertainty. Quantitative Finance, Taylor & Francis Journals, 17(12), 1933-1963.
  • Hutchison, M., & McDill, K. (1999). Are all banking crises alike? The Japanese experience in international comparison (Working Paper, No. 7253). Cambridge, MA: NBER. Retrieved August 14, 2020 from
  • Inske, P. (2016). Forecasting euro area recessions in real-time (Kiel Working Paper, No. 2020). Kiel: Kiel Institute for the World Economy. Retrieved August 14, 2020 from
  • (2020). Currencies. Retrieved August 14, 2020 from
  • Jarmulska, B. (2020). Random forest versus logit models: Which offers better early warning of fiscal stress? (Working Paper Series, No. 2408). Brussels: European Central Bank. Retrieved August 14, 2020 from
  • Kaminsky, G., & Reinhart, C. (1999). The twin crises: The causes of banking and balance-of-payments problems. The American Economic Review, 89(3), 473-500.
  • Kaminsky, G., Lizondo, S., & Reinhart, C. (1997). Leading indicators of currency crises (Staff Papers 45). Washington: IMF.
  • Krugman, P. (1979). A model of balance-of-payments crises. Journal of Money, Credit and Banking, 11(3), 311-325.
  • Łupiński, M. (2019). Wskaźniki wczesnego ostrzegania przed niestabilnością finansową polskiego sektora bankowego [Early warning indicators of the financial instability of the Polish banking sector]. Collegium of Economic Analysis Annals, 55, 99-113. Retrieved August 14, 2020 from
  • Manasse, P., Roubini, N., & Schimmelpfennig, A. (2003). Predicting sovereign debt crises (Working Paper, No. 221). Washington: IMF.
  • Marjanović, I., & Marković, M. (2019). Determinants of currency crises in the Republic of Serbia. Proceedings of Rijeka Faculty of Economics, 37(1), 191-212.
  • Minguez, J. G., & Carrascal, C. M. (2019). A crisis early warning model for euro area countries. Economic Bulletin, Banco de Espana, 4, 1-13. Retrieved August 14, 2020, from
  • Musdholifah, M., & Hartono, U. (2017). Assessing early warning system model for banking crisis in ASEAN countries. International Journal of Economics and Financial Issues, Econjournals, 7(4), 358-364. Retrieved August 14, 2020 from
  • Obstfeld, M. (1986). Rational and self-fulfilling balance-of-payments crises. The American Economic Review, 76(1), 72-81. Retrieved August 14, 2020 from
  • Obstfeld, M. (1995). Models of currency crises with self-fulfilling features (Working Paper, No. 5285). Washington: NBER.
  • OECD report. (2019). Argentina. OECD Economic surveys. Retrieved August 14, 2020, from
  • Papadopoulos, S., Stavroulias, P., Sager, T., & Baranoff, E. (2017). A ternary-state early warning system for the European Union (Working Papers, No. 222). Athens: Bank of Greece. Retrieved August 14, 2020 from
  • Peltonen, T. A., Sarlin, P., & Piloiu, A. (2015). Network linkages to predict bank distress (Working Paper Series, No. 1828). Brussels: European Central Bank.
  • Sigmund, M., & Stein, I. (2017). What predicts financial (in)stability? A Bayesian approach. Credit and Capital Markets, 50(3), 299-336.
  • Sondermann, D., & Zorell, N. (2019). A macroeconomic vulnerability model for the euro area (Working Paper Series, No. 2306). Brussels: European Central Bank. Retrieved August 14, 2020 from
  • Stanley, L. E. (2018). Argentina. In L. E. Stanley, Emerging market economies and financial globalization. Argentina, Brasil, China, India and South Korea (pp. 89- 109). London: Anthem Press.
  • The World Bank. (2019). Global Economic Monitor. Retrieved January 31, 2020 from
  • Tölö, E. (2019). Predicting systemic financial crises with recurrent neural networks (Research Discussion Papers, 14/2019). Bank of Finland.
  • Tomczyńska, M. (2000). Early indicators of currency crises. Review of some literature (Studies & Analyses CASE No. 208). Warsaw: Center for Social and Economic Research. Retrieved August 14, 2020 from
  • Wang, P., Zong, L., & Ma, Y. (2019). An integrated early warning system for stock market turbulence (Papers 1911.12596,
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