IDEAS home Printed from
   My bibliography  Save this paper

Long memory in the ukrainian stock market and financial crises


  • Maria Caporale, Guglielmo
  • Gil-Alana, Luis
  • Plastun, Alex
  • Makarenko, Inna


This paper examines persistence in the Ukrainian stock market during the recent financial crisis. Using two different long memory approaches (R/S analysis and fractional integration) we show that this market is inefficient and the degree of persistence is not the same in different stages of the financial crisis. Therefore trading strategies might have to be modified. We also show that data smoothing is not advisable in the context of R/S analysis.

Suggested Citation

  • Maria Caporale, Guglielmo & Gil-Alana, Luis & Plastun, Alex & Makarenko, Inna, 2013. "Long memory in the ukrainian stock market and financial crises," MPRA Paper 59061, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:59061

    Download full text from publisher

    File URL:
    File Function: original version
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    1. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
    2. Onali, Enrico & Goddard, John, 2011. "Are European equity markets efficient? New evidence from fractal analysis," International Review of Financial Analysis, Elsevier, vol. 20(2), pages 59-67, April.
    3. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    4. Emmanuel Anoruo & Luis Gil-Alana, 2011. "Mean reversion and long memory in African stock market prices," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 35(3), pages 296-308, July.
    5. Nuno Crato & Bonnie K. Ray, 2000. "Memory in returns and volatilities of futures' contracts," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 20(6), pages 525-543, July.
    6. Ignacio N Lobato & Carlos Velasco, 2007. "Efficient Wald Tests for Fractional Unit Roots," Econometrica, Econometric Society, vol. 75(2), pages 575-589, March.
    7. Velasco, Carlos, 2000. "Non-Gaussian Log-Periodogram Regression," Econometric Theory, Cambridge University Press, vol. 16(01), pages 44-79, February.
    8. Greene, Myron T. & Fielitz, Bruce D., 1977. "Long-term dependence in common stock returns," Journal of Financial Economics, Elsevier, vol. 4(3), pages 339-349, May.
    9. Hung‐Gay Fung & Wai‐Chung Lo, 1993. "Memory in interest rate futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 13(8), pages 865-872, December.
    10. Velasco, Carlos, 1999. "Non-stationary log-periodogram regression," Journal of Econometrics, Elsevier, vol. 91(2), pages 325-371, August.
    11. Marco Corazza & A. G. Malliaris, 2005. "Multi-Fractality in Foreign Currency Markets," World Scientific Book Chapters,in: Economic Uncertainty, Instabilities And Asset Bubbles Selected Essays, chapter 11, pages 151-184 World Scientific Publishing Co. Pte. Ltd..
    12. Yaya, OlaOluwa S. & Gil-Alana, Luis A., 2014. "The persistence and asymmetric volatility in the Nigerian stock bull and bear markets," Economic Modelling, Elsevier, vol. 38(C), pages 463-469.
    13. Serletis, Apostolos & Rosenberg, Aryeh Adam, 2007. "The Hurst exponent in energy futures prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 325-332.
    14. Abadir, Karim M. & Distaso, Walter & Giraitis, Liudas, 2007. "Nonstationarity-extended local Whittle estimation," Journal of Econometrics, Elsevier, vol. 141(2), pages 1353-1384, December.
    15. Lennart Berg & Johan Lyhagen, 1998. "Short and long-run dependence in Swedish stock returns," Applied Financial Economics, Taylor & Francis Journals, vol. 8(4), pages 435-443.
    16. Grech, Dariusz & Pamuła, Grzegorz, 2008. "The local Hurst exponent of the financial time series in the vicinity of crashes on the Polish stock exchange market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(16), pages 4299-4308.
    17. Billy P. Helms & Fred R. Kaen & Robert E. Rosenman, 1984. "Memory in commodity futures contracts," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 4(4), pages 559-567, December.
    18. Booth, G. Geoffrey & Kaen, Fred R. & Koveos, Peter E., 1982. "R/S analysis of foreign exchange rates under two international monetary regimes," Journal of Monetary Economics, Elsevier, vol. 10(3), pages 407-415.
    19. Berg, Lennart & Lyhagen, Johan, 1996. "Short and Long Run Dependence in Swedish Stock Returns," Working Paper Series 1996:19, Uppsala University, Department of Economics.
    20. B. Mandelbrot, 1972. "Statistical Methodology for Nonperiodic Cycles: From the Covariance To R/S Analysis," NBER Chapters,in: Annals of Economic and Social Measurement, Volume 1, number 3, pages 259-290 National Bureau of Economic Research, Inc.
    21. Robinson, Peter M. & Velasco, Carlos, 2000. "Whittle pseudo-maximum likelihood estimation for nonstationary time series," LSE Research Online Documents on Economics 2273, London School of Economics and Political Science, LSE Library.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Guglielmo Maria Caporale & Luis A. Gil-Alana & Alex Plastun, 2017. "Is Market Fear Persistent? A Long-Memory Analysis," Discussion Papers of DIW Berlin 1670, DIW Berlin, German Institute for Economic Research.
    2. Guglielmo Maria Caporale & Luis A. Gil-Alana & Alex Plastun, 2017. "Long Memory and Data Frequency in Financial Markets," Discussion Papers of DIW Berlin 1647, DIW Berlin, German Institute for Economic Research.

    More about this item


    persistence; long memory; R/S analysis; 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

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:59061. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.