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Fundamental Factors Affecting the MOEX Russia Index: Retrospective Analysis

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  • Lozinskaia, Agata
  • Saltykova, Anastasiia

Abstract

This paper is an empirical study of the changing nature of the de-pendence of fundamental factors on thestock market index, which is the trend identified earlier in the Russian stock market. We empirically test the impact of daily values of fundamental factors on the MOEX Russia Index from 2003 to 2018. The analysis of the ARIMA-GARCH (1,1) model with a rolling window reveals that the change in the power and direction of the influence of the fun-damental factors on the Russian stock market persists. The Quandt-Andrews breakpoint test and Bai-Perron test identify the number and likely location of structural breaks. We find multiple breaks probably associated with the dra-matic falls of the stock market index. The results of the regression models over the different regimes, defined by the structural breaks, can vary markedly over time. This research is of value in macroeconomic forecasting and in the invest-ment strategy development.

Suggested Citation

  • Lozinskaia, Agata & Saltykova, Anastasiia, 2019. "Fundamental Factors Affecting the MOEX Russia Index: Retrospective Analysis," MPRA Paper 97308, University Library of Munich, Germany, revised 23 Sep 2019.
    • Handle: RePEc:pra:mprapa:97308
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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. 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.
    3. 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.
    4. 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.
    5. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    6. 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.
    7. 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.
    8. Peresetsky, A. A., 2011. "What determines the behavior of the Russian stock market," MPRA Paper 41508, University Library of Munich, Germany.
    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.
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    More about this item

    Keywords

    Russian stock market; fundamental factors; structuralinstability; structural breaks; rolling regression; breakpoint tests;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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