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Long Memory in the Ukrainian Stock Market

Author

Listed:
  • Guglielmo Maria Caporale
  • Luis A. Gil-Alana

Abstract

This paper examines the dynamics of stock prices in Ukraine by estimating the degree of persistence of the PFTS stock market index. Using long memory techniques we show that the log prices series is I(d) with d slightly above 1, implying that returns are characterised by a small degree of long memory and thus are predictable using historical data. Moreover, their volatility, measured as the absolute and squared returns, also displays long memory. Finally, we examine if the time dependence is affected by the day of the week; the results indicate that Mondays and Fridays are characterised by higher dependency, consistently with the literature on anomalies in stock market prices.

Suggested Citation

  • Guglielmo Maria Caporale & Luis A. Gil-Alana, 2013. "Long Memory in the Ukrainian Stock Market," Discussion Papers of DIW Berlin 1279, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp1279
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    References listed on IDEAS

    as
    1. Lobato, Ignacio N & Savin, N E, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 261-268, July.
    2. Chris Brooks & Gita Persand, 2001. "Seasonality in Southeast Asian stock markets: some new evidence on day-of-the-week effects," Applied Economics Letters, Taylor & Francis Journals, vol. 8(3), pages 155-158.
    3. Robinson, P.M. & Henry, M., 1999. "Long And Short Memory Conditional Heteroskedasticity In Estimating The Memory Parameter Of Levels," Econometric Theory, Cambridge University Press, vol. 15(03), pages 299-336, June.
    4. Granger, Clive W. J. & Ding, Zhuanxin, 1996. "Varieties of long memory models," Journal of Econometrics, Elsevier, vol. 73(1), pages 61-77, July.
    5. Ignacio N Lobato & Carlos Velasco, 2007. "Efficient Wald Tests for Fractional Unit Roots," Econometrica, Econometric Society, vol. 75(2), pages 575-589, March.
    6. Jeffrey Jaffe & R. Westerfield, "undated". "The Week-End Effect in Common Stock Returns: The International Evidence," Rodney L. White Center for Financial Research Working Papers 3-85, Wharton School Rodney L. White Center for Financial Research.
    7. French, Kenneth R., 1980. "Stock returns and the weekend effect," Journal of Financial Economics, Elsevier, vol. 8(1), pages 55-69, March.
    8. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    9. Jeffrey Jaffe & R. Westerfield, "undated". "The Week-End Effect in Common Stock Returns: The International Evidence," Rodney L. White Center for Financial Research Working Papers 03-85, Wharton School Rodney L. White Center for Financial Research.
    10. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    11. Abadir, Karim M. & Distaso, Walter & Giraitis, Liudas, 2007. "Nonstationarity-extended local Whittle estimation," Journal of Econometrics, Elsevier, vol. 141(2), pages 1353-1384, December.
    12. Solnik, Bruno & Bousquet, Laurence, 1990. "Day-of-the-week effect on the Paris Bourse," Journal of Banking & Finance, Elsevier, vol. 14(2-3), pages 461-468, August.
    13. Lobato, Ignacio N & Savin, N E, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 280-283, July.
    14. Gibbons, Michael R & Hess, Patrick, 1981. "Day of the Week Effects and Asset Returns," The Journal of Business, University of Chicago Press, vol. 54(4), pages 579-596, October.
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    More about this item

    Keywords

    Stock market prices; Efficient market hypothesis; Long memory; Fractional integration;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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