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Score-driven stochastic seasonality of the Russian rouble: an application case study for the period of 1999 to 2020

Author

Listed:
  • Astrid Ayala

    (Universidad Francisco Marroquín)

  • Szabolcs Blazsek

    (Universidad Francisco Marroquín)

  • Adrian Licht

    (Universidad Francisco Marroquín)

Abstract

In this paper, score-driven time series models are used, in order to provide robust estimates of the seasonal components of Russian rouble (RUB) currency exchange rates for the period of 1999 to 2020. This paper is the first empirical application of score-driven models to the RUB to US dollar (USD) and RUB to Euro (EUR) currency exchange rates in the literature. The model includes score-driven local level, seasonality, and volatility components for a variety of probability distributions: Student’s t distribution, skewed generalized t (Skew-Gen-t) distribution, exponential generalized beta distribution of the second kind (EGB2), normal-inverse Gaussian (NIG) distribution, and Meixner (MXN) distribution. The use of the MXN distribution is new in the literature of score-driven seasonality models. We show that the score-driven models of this paper are robust to changes in the currency exchange rate regimes of the Bank of Russia. We find that the annual seasonality of the RUB is significant, and it is in the range of $$\pm 4\%$$ ± 4 % . We review the determinants of the RUB seasonality using data on exports, imports, and primary income from the current account of the Russian Federation. The statistical performances of all score-driven models are superior to the statistical performance of the classical multiplicative seasonal autoregressive integrated moving average (ARIMA) model. Our results may motivate the practical use of score-driven models of the RUB exchange rate seasonality for financing, investment, or policy decisions.

Suggested Citation

  • Astrid Ayala & Szabolcs Blazsek & Adrian Licht, 2022. "Score-driven stochastic seasonality of the Russian rouble: an application case study for the period of 1999 to 2020," Empirical Economics, Springer, vol. 62(5), pages 2179-2203, May.
    • Handle: RePEc:spr:empeco:v:62:y:2022:i:5:d:10.1007_s00181-021-02103-6
    DOI: 10.1007/s00181-021-02103-6
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    as
    1. John Geweke & Susan Porter‐Hudak, 1983. "The Estimation And Application Of Long Memory Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 4(4), pages 221-238, July.
    2. Ilja Viktorov & Alexander Abramov, 2020. "The 2014–15 Financial Crisis in Russia and the Foundations of Weak Monetary Power Autonomy in the International Political Economy," New Political Economy, Taylor & Francis Journals, vol. 25(4), pages 487-510, June.
    3. Michele Caivano & Andrew Harvey & Alessandra Luati, 2016. "Robust time series models with trend and seasonal components," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(1), pages 99-120, March.
    4. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    5. Mulatu Zerihun & Marthinus Breitenbach & Bernard Njindan Iyke, 2020. "A Comparative Analysis of Currency Volatility among BRICS Countries," Journal of African Business, Taylor & Francis Journals, vol. 21(1), pages 78-104, January.
    6. Mensah, Lord & Obi, Pat & Bokpin, Godfred, 2017. "Cointegration test of oil price and us dollar exchange rates for some oil dependent economies," Research in International Business and Finance, Elsevier, vol. 42(C), pages 304-311.
    7. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024, January.
    8. Bozhechkova Alexandra & Knobel Alexander & Trunin Pavel, 2021. "Balance of Payments in Q2 2021 [Платежный Баланс Во Втором Квартале 2021 Г]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 8, pages 4-8, August.
    9. Anne Opschoor & André Lucas, 2019. "Fractional Integration and Fat Tails for Realized Covariance Kernels," Journal of Financial Econometrics, Oxford University Press, vol. 17(1), pages 66-90.
    10. Tata Subba Rao & Granville Tunnicliffe Wilson & Andrew Harvey & Rutger-Jan Lange, 2017. "Volatility Modeling with a Generalized t Distribution," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(2), pages 175-190, March.
    11. Andrew Harvey & Rutger‐Jan Lange, 2018. "Modeling the Interactions between Volatility and Returns using EGARCH‐M," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 909-919, November.
    12. Szabolcs Blazsek & Adrian Licht, 2020. "Dynamic conditional score models: a review of their applications," Applied Economics, Taylor & Francis Journals, vol. 52(11), pages 1181-1199, March.
    13. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    14. Nadezhda Ivanova, 2007. "Estimation of the Equilibrium Real Exchange Rate in Russia: Trade-Balance Approach," Working Papers w0102, New Economic School (NES).
    15. Andrew Harvey & Alessandra Luati, 2014. "Filtering With Heavy Tails," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1112-1122, September.
    16. Szabolcs Blazsek & Han-Chiang Ho & Su-Ping Liu, 2018. "Score-driven Markov-switching EGARCH models: an application to systematic risk analysis," Applied Economics, Taylor & Francis Journals, vol. 50(56), pages 6047-6060, December.
    17. Mironov, Valeriy, 2015. "Russian devaluation in 2014–2015: Falling into the abyss or a window of opportunity?," Russian Journal of Economics, Elsevier, vol. 1(3), pages 217-239.
    18. Rautava, Jouko, 2004. "The role of oil prices and the real exchange rate in Russia's economy--a cointegration approach," Journal of Comparative Economics, Elsevier, vol. 32(2), pages 315-327, June.
    19. Bernardina Algieri, 2013. "Determinants of the real effective exchange rate in the Russian Federation," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 22(7), pages 1013-1037, October.
    20. Mr. Tomás J. T. Baliño & Mr. Jakob Horder & Mr. David S. Hoelscher, 1997. "Evolution of Monetary Policy Instruments in Russia," IMF Working Papers 1997/180, International Monetary Fund.
    21. Kirill Sosunov & Oleg Zamulin, 2006. "Can Oil Prices Explain the Real Appreciation of the Russian Ruble in 1998-2005?," Working Papers w0083, New Economic School (NES).
    22. Michele Caivano & Andrew Harvey, 2014. "Time-series models with an EGB2 conditional distribution," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(6), pages 558-571, November.
    23. Nadezhda Ivanova, 2007. "Estimation of the Equilibrium Real Exchange Rate in Russia: Trade-Balance Approach," Working Papers w0102, Center for Economic and Financial Research (CEFIR).
    24. V. Mironov., 2015. "Russian Devaluation in 2014—2015: Falling into the Abyss or Window of Opportunities?," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 12.
    25. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    More about this item

    Keywords

    Russian rouble; Current account of Russia; Currency exchange rate regimes of the Bank of Russia; Score-driven local level; seasonality; and volatility; Dynamic conditional score; Generalized autoregressive score;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • F31 - International Economics - - International Finance - - - Foreign Exchange

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