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Volatility of ruble exchange rate: Oil and sanctions

Author

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  • Artem Aganin

    (National Research University Higher School of Economics, Moscow, Russian Federation)

  • Anatoly Peresetsky

    (National Research University Higher School of Economics, Moscow, Russian Federation)

Abstract

Stability of the national currency exchange rate is an important factor for sustainable economic growth. A number of papers demonstrate that in the oil exporting countries currency exchange rate to the large extent is defined by the international oil prices. However, there are almost no papers which consider dependence of the exchange rate volatility on the volatility of oil prices. In this paper, we use one-dimensional GARCH models and two-dimensional VAR-BEKK models to analyze the dependence of the ruble exchange rate volatility on the oil price volatility. We found out that this dependence is not constant in time and depends on various macroeconomic factors. This dependence significantly increases when oil prices are low and weakens when oil prices are high. The introduction of sanctions has increased the volatility of the ruble exchange rate. Sanctions have also increased the dependence of the ruble exchange rate volatility on oil price volatility. It is also shown that the impact of sanctions decreases with time, what can be interpreted as an adaptation of the Russian economy to sanctions.

Suggested Citation

  • Artem Aganin & Anatoly Peresetsky, 2018. "Volatility of ruble exchange rate: Oil and sanctions," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 52, pages 5-21.
    • Handle: RePEc:ris:apltrx:0353
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    References listed on IDEAS

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    1. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    2. Iikka Korhonen & Tuuli Juurikkala, 2009. "Equilibrium exchange rates in oil-exporting countries," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 33(1), pages 71-79, January.
    3. Boldanov, Rustam & Degiannakis, Stavros & Filis, George, 2016. "Time-varying correlation between oil and stock market volatilities: Evidence from oil-importing and oil-exporting countries," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 209-220.
    4. Yang, Lu & Cai, Xiao Jing & Hamori, Shigeyuki, 2018. "What determines the long-term correlation between oil prices and exchange rates?," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 140-152.
    5. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    6. Ole E. Barndorff‐Nielsen & Neil Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280, May.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    8. Mironov, Valeriy V. & Petronevich, Anna V., 2015. "Discovering the signs of Dutch disease in Russia," Resources Policy, Elsevier, vol. 46(P2), pages 97-112.
    9. Ankudinov, Andrei & Ibragimov, Rustam & Lebedev, Oleg, 2017. "Sanctions and the Russian stock market," Research in International Business and Finance, Elsevier, vol. 40(C), pages 150-162.
    10. David E. Allen & Michael McAleer, 2018. "Theoretical and Empirical Differences between Diagonal and Full BEKK for Risk Management," Energies, MDPI, vol. 11(7), pages 1-19, June.
    11. 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.
    12. Komain Jiranyakul, 2015. "Oil Price Volatility and Real Effective Exchange Rate: The Case of Thailand," International Journal of Energy Economics and Policy, Econjournals, vol. 5(2), pages 574-579.
    13. Berna Aydoğan & Gökçe Tunç & Tezer Yelkenci, 2017. "The impact of oil price volatility on net-oil exporter and importer countries’ stock markets," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 7(2), pages 231-253, August.
    14. Bai, Shuming & Koong, Kai S., 2018. "Oil prices, stock returns, and exchange rates: Empirical evidence from China and the United States," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 12-33.
    15. Jong-Min Kim and Hojin Jung, 2018. "Dependence Structure between Oil Prices, Exchange Rates, and Interest Rates," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    16. Basher, Syed Abul & Haug, Alfred A. & Sadorsky, Perry, 2018. "The impact of oil-market shocks on stock returns in major oil-exporting countries," Journal of International Money and Finance, Elsevier, vol. 86(C), pages 264-280.
    17. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    18. repec:zbw:bofitp:2015_003 is not listed on IDEAS
    19. Fatih Kaplan, 2015. "Oil Price, Exhange Rate and Economic Growth in Russia: A Multiple Structural Break Approach," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 5(4), pages 1-7.
    20. Mironov, Valeriy V. & Petronevich, Anna V., 2015. "Discovering the signs of Dutch disease in Russia," Resources Policy, Elsevier, vol. 46(P2), pages 97-112.
    21. Wang, Yudong & Wei, Yu & Wu, Chongfeng & Yin, Libo, 2018. "Oil and the short-term predictability of stock return volatility," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 90-104.
    22. Gurvich, Evsey & Prilepskiy, Ilya, 2015. "The impact of financial sanctions on the Russian economy," Russian Journal of Economics, Elsevier, vol. 1(4), pages 359-385.
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    Cited by:

    1. Daniil Lomonosov & Andrey Polbin & Nikita Fokin, 2021. "The Impact of Global Economic Activity, Oil Supply and Speculative Oil Shocks on the Russian Economy," HSE Economic Journal, National Research University Higher School of Economics, vol. 25(2), pages 227-262.
    2. Dean Fantazzini & Tamara Shangina, 2019. "The importance of being informed: forecasting market risk measures for the Russian RTS index future using online data and implied volatility over two decades," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 55, pages 5-31.
    3. Salisu, Afees A. & Cuñado, Juncal & Gupta, Rangan, 2022. "Geopolitical risks and historical exchange rate volatility of the BRICS," International Review of Economics & Finance, Elsevier, vol. 77(C), pages 179-190.
    4. Kolesnikova, Anna & Fantazzini, Dean, 2021. "Asymmetry and hysteresis in the Russian gasoline market: The rationale for green energy exports," Energy Policy, Elsevier, vol. 157(C).
    5. Mikhail Makushkin & Victor Lapshin, 2020. "Modelling tail dependencies between Russian and foreign stock markets: Application for market risk valuation," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 57, pages 30-52.
    6. Ahmed Alwadeai & Nataliia Vlasova & Hadi Mareeh & Nadeem Aljonaid, 2024. "Beyond traditional defenses: Unraveling the dynamics of reserves and exchange rate volatility in the face of economic sanctions," Russian Journal of Economics, ARPHA Platform, vol. 10(1), pages 1-19, March.
    7. Oleg Sviridov & Alexander Skorobogatov, 2025. "A causal link analysis from oil price to the Russian stock market," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 77, pages 5-24.

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    More about this item

    Keywords

    exchange rate volatility; oil price volatility; Russia; sanctions; AR-TGARCH; VAR-BEKK;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • F51 - International Economics - - International Relations, National Security, and International Political Economy - - - International Conflicts; Negotiations; Sanctions
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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