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From Transition Crises to Macroeconomic Stability? Lessons from a Crises Early Warning System for Eastern European and CIS Countries

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Abstract

This paper uses a Markov regime-switching model to assess the vulnerability of a series of Central and Eastern European countries (ie Czech Republic, Hungary, Slovak Republic) and two CIS countries (ie, Russia and Ukraine) during the period 1993–2004. For the new EU member states in Central and Eastern Europe, the results of our model show that the majority of crises in those countries can be explained by inconsistencies in the domestic policy mix and by the deterioration of macroeconomic fundamentals, as emphasised by first-generation crises models, while for the CIS countries analysed, financial vulnerability type indicators were the most relevant, that is, indicators connected with the second- and third-generation of crisis model better explain the vulnerability of these countries. Additionally, the set of indicators chosen by our model is rather heterogeneous, supporting the superiority of a country-by-country approach.

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Notes

  1. The IMF's EWS is described in Berg et al. (1999).

  2. As explained later in the paper, from the group of Central and Eastern European countries we excluded Poland, because of data availability. Other countries from the region (ie, the Baltics, Belarus, and Bulgaria) were excluded due to the lack of open crisis during the period of data availability.

  3. For a comprehensive review of the theoretical literature for the first- and second-generation crises models, see Blackburn and Sola (1993), Flood and Marion (1999) and Jeanne (2000).

  4. What triggers the jump between multiple equilibria remains largely unexplained. Possible explanations are contagion effects or herding behaviour in the presence of imperfect information, see Masson (1999).

  5. Kaminsky and Reinhart (1996, 1999) pioneered the empirical work on twin crises. They found empirical evidence that banking crises tend to precede currency crises, but the causal link is not unidirectional since the currency crisis deepens the banking crisis.

  6. See Corsetti et al. (1998), Dooley (1997), Krugman (1998) and McKinnon and Pill (1997).

  7. See Chang and Velasco (1998), Goldfajn and Valdés (1997) and Radelet and Sachs (1998).

  8. For an extensive survey of the empirical literature, see for example Kaminsky et al. (1998) and Abiad (2003).

  9. Kaminsky (1998) presents a method to combine individual indicators into a composite indicator.

  10. In addition to the studies mentioned, Alvarez-Plata and Schrooten (2003), Jeanne and Masson (2000) and Fratzscher (1999) use Markov-switching models with constant transition probabilities to model the switches between multiple equilibria leading to currency crises.

  11. At a cut-off probability of 50%, the model correctly calls 65% of pre-crisis periods, whereby 27% of total alarm signals are false.

  12. The model correctly calls approximately 70% of pre-crisis periods at a cut-off probability of 40%.

  13. Diebold et al. (1993) extended the baseline Hamilton (1989) regime-switching model to allow for time-varying transition probabilities.

  14. Here, it is assumed that the indicators that influence the crisis probability neither worsen nor improve during this period.

  15. Results not presented here, but available from the authors upon request.

  16. Each indicator is standardised to be zero mean and unit variance.

  17. Results not presented here, but available from the authors upon request.

  18. At the beginning of 1997, the estimations for current account deficit to GDP for the whole of 1997 were around 10%, far exceeding the expected inflows of long-term non-debt capital.

  19. Gibson and Tsakalaatos (2004: 577).

  20. The rate of devaluation in the crawling band regime decreased continuously, from 0.060% of daily devaluation in March 1995 to 0.00654% of daily devaluation in April 2001.

  21. The first base rate increase took place in May, while the second was at the end of November. In both cases, the increase of the base interest rate was 300 basis points.

  22. The estimated costs of the removal of non-performing loans are about 105 billion Slovak crowns (about 12% of the nominal GDP in 1999).

  23. After the break-up of Czechoslovakia and the following monetary separation, both countries pegged their currencies to baskets with relatively narrow oscillation bands (±0.5% from central parity).

  24. During May 2002, the central bank decided to increase interest rates to cool down excessive demand pressures.

  25. The goodness-of-fit values differ somewhat ranging between 88% in Russia to 69% in Hungary for all observations. The results are most homogeneous for calling the tranquil periods, that is, above 80% success rate for all countries.

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Authors and Affiliations

  1. Sovereign Risk Unit, Moody's Deutschland, An der Welle 5, Frankfurt/Main, D-60322, Germany

    Kristina Kittelmann

  2. Banking and International Finance Department, Faculty of National Economy, University of Economics, Bratislava, Slovakia

    Marcel Tirpak

  3. Kiel Institute for World Economics, Duesternbrooker Weg 120, Kiel, D-24105, Germany

    Rainer Schweickert

  4. Kiel Institute for World Economics and Head, Russia/Belarus Desk, DG-ECFIN, European Commission, Avenue de Beaulieu, 1, Brussel, B-1160, Belgium

    Lúcio Vinhas De Souza

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  1. Kristina Kittelmann
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  2. Marcel Tirpak
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  4. Lúcio Vinhas De Souza
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Kittelmann, K., Tirpak, M., Schweickert, R. et al. From Transition Crises to Macroeconomic Stability? Lessons from a Crises Early Warning System for Eastern European and CIS Countries. Comp Econ Stud 48, 410–434 (2006). https://doi.org/10.1057/palgrave.ces.8100162

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