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Drivers of Exchange Rate Dynamics in Selected CIS Countries: Evidence from a FAVAR Analysis

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  • Christian Dreger
  • Jarko Fidrmuc

Abstract

We investigate the likely sources of exchange rate dynamics in selected CIS countries (Russia, Kazakhstan, Ukraine, Kyrgyzstan, Azerbaijan, and Moldova) over the past decade (1999-2008). The analysis is based on country VAR models 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 global drivers such as gold, oil and share prices. Global, regional and idiosyncratic shocks are identified in a standard Cholesky fashion. Based on the decomposition of the variance of forecast errors, their relevance for exchange rates is explored. As a quite robust finding, CIS exchange rates have become more vulnerable to global shocks towards the end of the sample.

Suggested Citation

  • Christian Dreger & Jarko Fidrmuc, 2009. "Drivers of Exchange Rate Dynamics in Selected CIS Countries: Evidence from a FAVAR Analysis," Discussion Papers of DIW Berlin 867, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp867
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    Cited by:

    1. Jarko Fidrmuc & Philipp J. Süss, 2011. "The Outbreak of the Russian Banking Crisis," Czech Economic Review, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, vol. 5(1), pages 046-063, March.
    2. Frank Hespeler, 2013. "A VECM evaluation of monetary transmission in Uzbekistan," Economic Change and Restructuring, Springer, vol. 46(2), pages 219-253, May.
    3. Christian Dreger & Jarko Fidrmuc & Konstantin Kholodilin & Dirk Ulbricht, 2015. "The Ruble between the Hammer and the Anvil: Oil Prices and Economic Sanctions," Discussion Papers of DIW Berlin 1488, DIW Berlin, German Institute for Economic Research.
    4. 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," Journal of Comparative Economics, Elsevier, vol. 44(2), pages 295-308.

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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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