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How to de-dollarize financial systems in the Caucasus and Central Asia?

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Abstract

Dollarization rates in the Caucasus and Central Asia (CCA) region are among the highest in the world, with adverse consequences for macroeconomic stability, monetary policy transmission, and financial sector development. Using dynamic panel data models, we find that foreign exchange deposits and loans in the CCA are mainly driven by volatile inflation and exchange rates, low financial depth, and asymmetric exchange rate policies biased toward depreciation. Although there is no unique formula for success, empirical studies and cross-country experiences suggest that credible monetary and exchange rate frameworks, low and stable inflation, and deep domestic financial markets are essential ingredients of any de-dollarization strategy. In implementation, policymakers need to consider proper sequencing of policies, effective communication as well as risks from potential financial disintermediation and instability, and/or capital flight.

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Fig. 1

Sources: IMF Financial Stability Indicators, and Authors Calculations

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Notes

  1. CCA countries are Armenia, Azerbaijan, Georgia, Kazakhstan, the Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan.

  2. For more details, see Kokenyne et al. (2010), Erasmus et al. (2009), Cayazzo et al. (2006), and Yeyati (2006).

  3. Ideally, one would have used realized volatility methods, as opposed to the recursive calculation of the variance, to measure volatility. This, however, typically requires data points at very high frequencies (see Andersen et al. 2001 who introduced this methodology). Furthermore, our relatively simple method of using the rolling standard deviation of inflation over the previous four quarters is widely used in country and regional reports by the IMF and World Bank (see for instance IMF WEO reports).

  4. It is worth mentioning that some papers include an interest rate differential variable that measures the effect of the difference between interest rates on domestic versus foreign deposits and loans. It would have also been ideal to include a measure of the depth of the foreign exchange market such as the size of foreign exchange (or derivatives) market. We were not able to incorporate these variables due to the lack of sufficient data for a number of countries in our dataset, lack of consistency in definitions of interest rate variables across all countries, as well as the different data frequencies available for different countries.

  5. For some countries where quarterly GDP data were not available (as in Turkmenistan and Uzbekistan), we use the Denton method, described in Di Fonzo and Marini (2012), to extrapolate yearly GDP into quarterly series.

  6. GMM estimators are typical in dynamic panel data models with small T and large N samples such as ours, where they overcome the Nickell bias, by Nickell (1981) who shows that the demeaning process creates a correlation between regressor and error that biases the estimate of the coefficient of the lagged dependent variable. Furthermore, Arellano and Bond (1991) show that by using instrumental variables in a GMM context, one can construct more efficient dynamic panel estimates.

  7. Tables 4 and 5 report Fisher panel unit root tests developed by Choi (2001) which allows for unbalanced panel models. The inverse Chi-squared statistic reported is highly significant in all models for all regions, implying a rejection of the null that all panels contain a unit root and conclude that the series is stationary. We use the default Newey-West number of lags suggested by the Phillips–Perron unit root test.

  8. Chicago Board Options Exchange, CBOE Volatility Index: VIX [VIXCLS], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/VIXCLS.

  9. Board of Governors of the Federal Reserve System (USA), Effective Federal Funds Rate [FEDFUNDS], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/FEDFUNDS.

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Authors and Affiliations

  1. Internal Economic Training Unit, Institute for Capacity Development, 700 19th Street, N.W., Washington, DC, 20431, USA

    Sami Ben Naceur

  2. International Monetary Fund, 700 19th Street, N.W., Washington, DC, 20431, USA

    Amr Hosny

  3. Loomis, Sayles & Company, L.P., One Financial Center, 31st Floor, Boston, MA, 02111, USA

    Gregory Hadjian

Authors
  1. Sami Ben Naceur
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  2. Amr Hosny
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  3. Gregory Hadjian
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Correspondence to Amr Hosny.

Additional information

The authors would like to thank Hanan Altimini, Neil Hickey, Annamaria Kokenyne Ivanics, Heedon Kang, Kia Penso, Cecilia Prado de Guzman, Damiano Sandri, Natalia Tamirisa, Romain Michel Veyrune, and James Walsh for their useful comments and feedback on an earlier version of this paper. The authors would also like to thank the editor and an anonymous referee for very constructive comments. All errors and omissions are our own. The views expressed in this paper are those of the authors and should not be attributed to the IMF, its Executive Board, or its management.

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Ben Naceur, S., Hosny, A. & Hadjian, G. How to de-dollarize financial systems in the Caucasus and Central Asia?. Empir Econ 56, 1979–1999 (2019). https://doi.org/10.1007/s00181-018-1419-6

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