The causality relationship between remittances and the real effective exchange rate: the case of the Kyrgyz Republic

  • Mirzosaid SultonovEmail author
Research Article


We assessed the causality relationship between the inflow of remittances and the real effective exchange rate (REER) of the Kyrgyz Republic, one of the most remittance-dependent economies in the world. We utilised the procedure suggested by Hong [15]. In the first step, we estimated univariate generalised autoregressive conditionally heteroskedasticity (GARCH) models for the logarithmic difference of the variables and saved the centred standardised residuals and their squared values. In the second step, we computed the sample cross-correlation function (CCF) between the standardised residuals and squared standardised residuals. Finally, we calculated Hong’s [15] Q-statistic and compared it to the upper tailed critical value of N(0;1) at an appropriate level. Descriptive statistics showed more volatile standard deviations for remittances compared with the REER. The skewness values indicated that increases are more likely to occur for the REER and decreases are more likely to occur for remittances. High kurtosis values suggested the existence of heavy tails in the return distribution. The skewness and kurtosis values showed that the returns are not normally distributed. At the 1% significance level, the Lagrange Multiplier (LM) test for autoregressive conditional heteroskedasticity (ARCH) rejected the null hypothesis of ‘no ARCH effects’ for both variables. At the 1–5% significance level, the augmented Dickey–Fuller (ADF) test rejected the null hypothesis that a unit root is present in the return series of both variables. The test for a structural break (a supremum Wald test) in the return series, without imposing a known break date, rejected the null hypothesis of no structural break at the 5% and 1% levels for the REER and remittances, respectively. The estimated break date for the REER was May 2015, and for the remittances, it was February 2008. The logarithmic differences of seasonally adjusted real monthly data for the period extending from January 2005 to December 2017 were used in the estimations. While the existing literature has focused mostly on the impact of remittances on the exchange rate (a unidirectional causality in the mean from remittances on the exchange rate), we examined how remittances cause the REER in the mean and variance as well as how the REER causes remittances in the mean and variance. The derived results did not reveal evidence of causality in the mean or variance from the remittances to the REER for the case of the Kyrgyz Republic. That means the changes and volatility of the remittances did not contain useful information for predicting the REER. On the contrary, the REER caused remittances in the mean and variance. That means past information and the volatility of the REER were useful for predicting remittances’ returns and volatility.


Remittance REER The Kyrgyz Republic Causality 



The author thanks the discussant Professor Satake M. (Doshisha University), participants in the monetary and financial policy session of the 17th International Conference of the Japan Economic Policy Association (JEPA) and two anonymous referees for their helpful comments and suggestions. The author alone is responsible for any errors that may remain.


  1. 1.
    Abazov, R. (2009). Current trends in migration in the Commonwealth of Independent States. UNDP Human Development Research Paper 2009/36. New York: UNDP. Accessed 5 Mar 2018.
  2. 2.
    Acosta, P., Lartey, E., & Mandelman, F. (2009). Remittances and the Dutch disease. Journal of International Economics, 79(1), 102–116.CrossRefGoogle Scholar
  3. 3.
    Ali, H. S., Law, S. H., Yusop, Z., Zeqiraj, V., Kofarmata, Y. I., & Abdulkarim, F. M. (2018). Remittance and growth nexus: bootstrap panel granger-causality evidence from high remittance receiving countries. International Journal of Economics and Business Research Inderscience Enterprises Ltd, 15(3), 312–324.CrossRefGoogle Scholar
  4. 4.
    Amuedo-Dorantes, C., & Pozo, S. (2004). Workers’ remittances and the real exchange rate: A paradox of gifts. World Development, 32(8), 1407–1417.CrossRefGoogle Scholar
  5. 5.
    Andrews, D. W. K. (1993). Tests for parameter instability and structural change with unknown change point. Econometrica, 61, 821–856.CrossRefGoogle Scholar
  6. 6.
    Atabaev, N., Atabaeva, G., & Baigonushova, D. (2014). Economic growth and remittances inflow: Empirical evidence from the Kyrgyz Republic. Eurasian Journal of Business and Economics, 7(14), 61–70.CrossRefGoogle Scholar
  7. 7.
    Ball, C., Cruz-Zuniga. M., Lopez, C., & J. Reyes. (2008). Remittances, inflation and exchange rate regimes in small open economies. University of Cincinnati, Economics Working Papers Series 2008-03, University of Cincinnati, Department of Economics.Google Scholar
  8. 8.
    Barajas, A., Chami, R., Hakura, D. and P. Montiel. (2010). Workers’ remittances and the equilibrium real exchange rate: Theory and evidence. Department of Economics Working Papers 2010-14, Department of Economics, Williams College.Google Scholar
  9. 9.
    Baum, C. F. (2006). An introduction to modern econometrics using Stata. College Station: Stata Press.Google Scholar
  10. 10.
    Bollerslev, T. (1986). Generalised autoregressive conditional hetroscedasticity. Journal of Econometrics, 31, 307–327.CrossRefGoogle Scholar
  11. 11.
    Cheung, Y. W., & Ng, L. K. (1996). A causality-in-variance test and its application to financial market prices. Journal of Econometrics, 72, 33–48.CrossRefGoogle Scholar
  12. 12.
    Dickey, A. D., & Fuller, A. W. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431.CrossRefGoogle Scholar
  13. 13.
    Dickey, A. D., & Fuller, A. W. (1981). Likelihood ration statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057–1072.CrossRefGoogle Scholar
  14. 14.
    Espinosa-Bowen, J., Ilahi, N., & F. Alturki. (2009). How Russia affects the neighborhood-trade, financial, and remittance channels. IMF Working Papers 09/277. Washington DC: IMF. Accessed 7 Mar 2018.
  15. 15.
    Hong, Y. (2001). A test for volatility spillover with application to exchange rates. Journal of Econometrics, 103(1–2), 183.CrossRefGoogle Scholar
  16. 16.
    Kamar, B., Bakardzhieva, D., & S. Naceur. (2010). The impact of capital and foreign exchange flows on the competitiveness of developing countries. IMF Working Papers 10/154, International Monetary Fund.Google Scholar
  17. 17.
    Ljung, G. M., & Box, G. E. P. (1978). On a measure of lack of fit in time series models. Biometrika, 65, 297–303.CrossRefGoogle Scholar
  18. 18.
    Lopez, H., Bussolo, M., & L. Molina. (2007). Remittances and the real exchange rate. World Bank Policy Research Working Paper No. 4213. Available at SSRN: Accessed 7 Mar 2018.
  19. 19.
    National Bank of the Kyrgyz Republic. (2018). Remittances. Bishkek: National Bank of the Kyrgyz Republic.Google Scholar
  20. 20.
    National Bank of the Kyrgyz Republic. (2018). Real effective exchange rate. Bishkek: National Bank of the Kyrgyz Republic.Google Scholar
  21. 21.
    Sultonov, M. (2016). The Russian financial crisis and workers’ remittances to Tajikistan and the Kyrgyz Republic. Journal of Reviews on Global Economics, 5, 344–353.CrossRefGoogle Scholar

Copyright information

© Japan Economic Policy Association (JEPA) 2019

Authors and Affiliations

  1. 1.Tohoku University of Community, Service and ScienceSakataJapan

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