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Fundamental Factors Affecting The Moex Russia Index: Structural Break Detection In A Long-Term Time Series

Author

Listed:
  • Agata Lozinskaia

    (National Research University Higher School of Economics)

  • Anastasiia Saltykova

    (National Research University Higher School of Economics)

Abstract

This paper studies how the influence of the fundamental factors on the Russian stock market changes retrospectively. We empirically test the impact of daily values of fundamental factors (indexes of foreign stock markets, oil price, exchange rate and interest rates in Russia and the USA) on the MOEX Russia Index over long time interval from 2003 to 2018. The analysis of the ARIMA-GARCH (1,1) model with a rolling window reveals the changes in the power and direction of the influence of the fundamental factors which are probably caused by the structural instability revealed earlier in Russia and other stock markets. The Quandt-Andrews breakpoint test and Bai-Perron test identify the number and likely location of the structural breaks. We find multiple breaks probably associated with dramatic falls in the stock market index, for example with the significant falls of the then MICEX index in the spring of 2006 and the global financial crisis of 2008-2009. The results of the regression models over the different regimes, defined by the structural breaks, can vary markedly over time.

Suggested Citation

  • Agata Lozinskaia & Anastasiia Saltykova, 2019. "Fundamental Factors Affecting The Moex Russia Index: Structural Break Detection In A Long-Term Time Series," HSE Working papers WP BRP 77/FE/2019, National Research University Higher School of Economics.
    • Handle: RePEc:hig:wpaper:77/fe/2019
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    References listed on IDEAS

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    1. Bernd Hayo & Ali M. Kutan, 2005. "The impact of news, oil prices, and global market developments on Russian financial markets," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 13(2), pages 373-393, April.
    2. Peresetsky, A. A., 2011. "What determines the behavior of the Russian stock market," MPRA Paper 41508, University Library of Munich, Germany.
    3. Iikka Korhonen & Anatoly Peresetsky, 2016. "What Influences Stock Market Behavior in Russia and Other Emerging Countries?," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 52(5), pages 1210-1225, May.
    4. Elena Andreou & Eric Ghysels, 2002. "Detecting multiple breaks in financial market volatility dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 579-600.
    5. Pesaran, M. Hashem & Timmermann, Allan, 2002. "Market timing and return prediction under model instability," Journal of Empirical Finance, Elsevier, vol. 9(5), pages 495-510, December.
    6. Mirzosharif JALOLOV & Tatsuyoshi MIYAKOSHI, 2005. "Who Drives The Russian Financial Markets?," The Developing Economies, Institute of Developing Economies, vol. 43(3), pages 374-395, September.
    7. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    8. Anatolyev, Stanislav, 2008. "A 10-year retrospective on the determinants of Russian stock returns," Research in International Business and Finance, Elsevier, vol. 22(1), pages 56-67, January.
    9. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    10. Sonin, Konstantin & Goriaev, Alexei P., 2005. "Is Political Risk Company-Specific? The Market Side of the Yukos Affair," CEPR Discussion Papers 5076, C.E.P.R. Discussion Papers.
    11. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
    12. Rapach, David E. & Wohar, Mark E., 2006. "In-sample vs. out-of-sample tests of stock return predictability in the context of data mining," Journal of Empirical Finance, Elsevier, vol. 13(2), pages 231-247, March.
    13. de Vries, Lisette & Gensler, Sonja & Leeflang, Peter S.H., 2012. "Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing," Journal of Interactive Marketing, Elsevier, vol. 26(2), pages 83-91.
    14. Daniel Smith, 2008. "Testing for structural breaks in GARCH models," Applied Financial Economics, Taylor & Francis Journals, vol. 18(10), pages 845-862.
    15. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    16. Solntsev, O. & Mamonov, M. & Pestova, A. & Magomedova, Z., 2011. "Experience in Developing Early Warning System for Financial Crises and the Forecast of Russian Banking Sector Dynamic in 2012," Journal of the New Economic Association, New Economic Association, issue 12, pages 41-76.
    17. Самойлов Дмитрий Васильевич, 2010. "Факторы, Оказывающие Влияние На Индекс Ртс Во Время Финансового Кризиса 2008-2009 Гг. И До Него," Higher School of Economics Economic Journal Экономический журнал Высшей школы экономики, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 14(2), pages 244-267.
    18. Chung-Ming Kuan, 2013. "Markov switching model (in Russian)," Quantile, Quantile, issue 11, pages 13-40, December.
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    More about this item

    Keywords

    Russian stock market; the MOEX Russian index; fundamental factors; structural breaks; long-term time series; rolling regression; breakpoint tests;
    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
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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