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Drivers of Exchange Rate Dynamics in Selected CIS Countries: Evidence from a Factor-Augmented Vector Autoregressive (FAVAR) Analysis

Author

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  • Christian Dreger
  • Jarko Fidrmuc

Abstract

We investigate the likely sources of exchange rate dynamics in selected member countries of the Commonwealth of Independent States (CIS; Russia, Kazakhstan, Ukraine, Kyrgyzstan, Azerbaijan, and Moldova) over the past decade (1999-2010). Evidence is based on country VARs augmented by a regional common-factor structure (FAVAR model). The models include nominal exchange rates, the common factor of exchange rates in the CIS countries, and international drivers such as global trade, share prices, and oil price. Global, regional, and idiosyncratic shocks are identified in a standard Cholesky fashion. Their relevance for exchange rates is explored by a decomposition of the variance of forecast errors. The impact of global shocks on the development of exchange rates has increased, particularly if financial shocks are considered. Because of the recent global financial crisis, regional shocks have become more important at the expense of global shocks.

Suggested Citation

  • Christian Dreger & Jarko Fidrmuc, 2011. "Drivers of Exchange Rate Dynamics in Selected CIS Countries: Evidence from a Factor-Augmented Vector Autoregressive (FAVAR) Analysis," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 47(4), pages 49-58, July.
    • Handle: RePEc:mes:emfitr:v:47:y:2011:i:4:p:49-58
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    Cited by:

    1. Mehmet Balcilar & Rangan Gupta & Clement Kyei & Mark E. Wohar, 2016. "Does Economic Policy Uncertainty Predict Exchange Rate Returns and Volatility? Evidence from a Nonparametric Causality-in-Quantiles Test," Open Economies Review, Springer, vol. 27(2), pages 229-250, April.
    2. Dreger, Christian & Kholodilin, Konstantin A. & Ulbricht, Dirk & Fidrmuc, Jarko, 2016. "Between the Hammer and the Anvil: The Impact of Economic Sanctions and Oil Prices on Russia’s Ruble," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 44(2), pages 295-308.
    3. Viktar Dudzich, 2022. "Real Exchange Rate Misalignments and Currency Crises in the Former Soviet Union Countries," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 64(3), pages 384-416, September.
    4. Hyeyoen Kim & Doojin Ryu, 2013. "Forecasting Exchange Rate from Combination Taylor Rule Fundamental," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 49(S4), pages 81-92, September.
    5. Danglun Luo & Qianwei Ying, 2014. "Political Connections and Bank Lines of Credit," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 50(03), pages 5-21, May.
    6. Skorepa, Michal & Komarek, Lubos, 2015. "Sources of asymmetric shocks: The exchange rate or other culprits?," Economic Systems, Elsevier, vol. 39(4), pages 654-674.
    7. Oleksandr Faryna & Heli Simola, 2018. "The Transmission of International Shocks to CIS Economies: A Global VAR Approach," Working Papers 04/2018, National Bank of Ukraine.
    8. Faryna, Oleksandr & Simola, Heli, 2021. "The transmission of international shocks to CIS economies: A global VAR approach," Economic Systems, Elsevier, vol. 45(2).
    9. Li, Kaifeng & Devpura, Neluka & Cheng, Sijia, 2022. "How did the oil price affect Japanese yen and other currencies? Fresh insights from the COVID-19 pandemic," Pacific-Basin Finance Journal, Elsevier, vol. 75(C).

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    Keywords

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    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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