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The determinants of vulnerability to currency crises: country-specific factors versus regional factors

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Abstract

We investigate the determinants of exchange market pressures (EMP) for some new EU member states at both the national and regional levels, where macroeconomic and financial variables are considered as potential sources. The regional common factors are extracted from these variables by using dynamic factor analysis. The linear empirical analysis, in general, highlights the importance of country-specific factors to defend themselves against vulnerability in their external sectors. Yet, given a significant impact of the common component in credit on EMP, a contagion effect is apparent through the conduit of credit market integration across these countries under investigation.

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Notes

  1. We focus on the ‘first’ wave of new EU member states in the CEE region, which are the largest by the GDP measure and geographically close to each other.

  2. Hungary and Poland went from a fixed exchange rate regime with varying bands to a managed or full floating rate system. In the case of the Czech Republic and Slovakia, the currency crises forced them to introduce floating exchange rates. The Maastricht exchange rate criterion implies a participation in the ERM II for new EU countries as a prerequisite to joining the single currency. Slovenia opted for the ERM II in 2004 from the managed floating system, and joined the euro in 2007. Slovakia also adopted the euro in January 2009.

  3. Note that Stavárek (2008) focuses on the model comparison of deriving EMP, which is different from our objective in this paper. Although Van Poeck et al. (2007) analyse the determinants of EMP for eight CEEC countries, their study is confined to country-specific factors without paying attention to regional factors. A number of observations is also rather limited by using quarterly data over the period 1990 and 2003.

  4. In the early warning literature, discrete models are often employed in the empirical work. It is argued that a discrete measure of crises in the binary models leads to a loss of information on the scale of speculative pressure, as it excludes incidents below the arbitrary threshold value. Such a constraint is avoided in the linear regression. In recent years, Markov switching model (MSM) has been applied to the currency crisis analysis. The MSM may have the ability of detecting the turning points between tranquil and speculative attack periods that are indicated by low and high regimes of volatility in EMP, respectively. The major limitation in the MSM is that it is extremely difficult to obtain a plausible result by specifying all potential determinants in the model. We resort our empirical analysis to the linear model.

  5. For example, it is argued that crises were associated with expansionary monetary and fiscal policies and also excessive domestic credit, leading to a substantial loss of foreign reserves under a fixed exchange rate regime (Krugman 1979).

  6. Kaminsky et al. (1998) follow the concept of Eichengreen et al. though without specifying interest rate differentials in their index. Edison (2003) extends the country coverage and adds several explanatory variables to develop this monitor system.

  7. Any lags that have more than 2 would make the computation inapplicable since there are five factors included in the dynamic factor model in this paper.

  8. Until the end of 1998, the exchange rate is against the ECU and after that, with the Euro.

  9. Note that the regional stress index, or the common EMP is the same for all five countries.

  10. This evidence provides the meaningfulness of modelling the determinants of individual EMPs based on both country-specific factors and common factors.

  11. See Mallick and Moore (2008) for the extensive empirical work for 60 developing countries.

  12. See Moore and Wang (2007); Wang and Moore (2009).

  13. Mody and Taylor (2007) attribute this to the moral hazard problem existing where financial institutions provide loans to finance risky financial assets, causing asset inflation beyond the level of fundamentals. When the bubble bursts, the consequence is capital flight triggering a currency crisis.

  14. We have checked the variables by the Augmented Dickey Fuller unit root test.

  15. In terms of fl, we extracted the common factor from the ratio of financial liability to GDP, rather than from the ratio of financial liability to money stock. This is due to the fact that the latter fails to converge in the dynamic factor model, and we were unable to obtain the common factor. As to fdi, the data are only available annually, hence we only specify the country-specific fdi.

  16. This is a test for breaks without known break dates. We tested for every year to find insignificant structural shifts, but in order to save space, we only present the test for every 2 years.

  17. It is likely that the explanatory variables that are specified in the model such as stock prices and domestic credit themselves take account of the structural shifts.

  18. It is, however, noted that the model may potentially suffer from inconsistency problem, since the common factors are the estimates. This caveat should be born in mind in interpreting the empirical results.

  19. It is also argued that central banks of these countries have not much intervened in the foreign exchange market.

  20. Hungary’s banking system was heavily exposed to foreign financing at a time when investors were pulling back from emerging economies during the financial crisis of October 2008. Hungary became the first European Union country to finalise an emergency rescue by securing $25 billion from the IMF, the EU and the World Bank.

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Acknowledgments

The authors are grateful to participants of this conference for their helpful comments. Thanks are also due to two anonymous referees of this journal for their helpful comments and suggestions.

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Authors and Affiliations

  1. Birmingham Business School, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

    Ping Wang

  2. Department of Economics and Finance, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK

    Tomoe Moore

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  1. Ping Wang
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  2. Tomoe Moore
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Correspondence to Tomoe Moore.

Appendix: Description of the data

Appendix: Description of the data

Data are collected from International Financial Statistics (code) and Datastream (DS):

National currency per US$ (rf, IFS). Nominal exchange rate with ecu/euro (DS). Foreign exchange reserves (id.d, IFS). Foreign liabilities (16c, IFS). Domestic credit (32, IFS). Consumer price index (64, IFS). Money plus quasi money (35L, IFS) for the Czech, Poland and Slovakia and M2 (DS) for Hungary and Base money (DS) for Slovenia. Industrial production (66, IFS) is used for GDP. Crude oil-Brent FOB US$ per Barrel (DS). Short term interest rates (DS). Share prices index (62) for Poland and DS market for the Czech republic and Hungary, SAX 16 for Slovakia and Slovenian Exchange Stock for Slovenia.

Some parts of data are calibrated as follows:

Monthly data for Financial liabilities (16c) and Domestic credit (32) in Hungary are not available during 1994:01–1999:12. The monthly data are interpolated from the corresponding quarterly series applying a linear technique. Monthly data of Domestic credit (32) are not available during 2005:1–2005:12. The missing data are calibrated proportionately with the same rate of growth as the data for Domestic credit to private sector (32d).

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Wang, P., Moore, T. The determinants of vulnerability to currency crises: country-specific factors versus regional factors. Empirica 41, 619–640 (2014). https://doi.org/10.1007/s10663-013-9218-y

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