IDEAS home Printed from https://ideas.repec.org/p/vuw/vuwecf/18828.html
   My bibliography  Save this paper

On long memory behaviour and predictability of financial markets

Author

Listed:
  • Vo, Long H.
  • Roberts, Leigh

Abstract

An immediate consequence of the Efficient Market Hypothesis (EMH) is the absence of auto-correlation of the return series of the financial prices and the exclusion of excess profitability made by any (active) trading strategy. However, the precondition for the validity of EMH, which assumes that all market participants can promptly receive and rationally react to the relevant information affecting the prices, might be (approximately) true for a long time horizon, but not for a short time horizon. By examining local long-range dependence (measured by the rolling Rescaled Range estimates of the Hurst index) of an empirical example, the local market inefficiency is inferred, and excess profitability of a simple trend-following trading strategies implies the potential for constructing a more profitable trading system by incorporating the former into the latter.

Suggested Citation

  • Vo, Long H. & Roberts, Leigh, 2014. "On long memory behaviour and predictability of financial markets," Working Paper Series 18828, Victoria University of Wellington, School of Economics and Finance.
    • Handle: RePEc:vuw:vuwecf:18828
    as

    Download full text from publisher

    File URL: https://ir.wgtn.ac.nz/handle/123456789/18828
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Malliaropulos, Dimitrios & Priestley, Richard, 1999. "Mean reversion in Southeast Asian stock markets," Journal of Empirical Finance, Elsevier, vol. 6(4), pages 355-384, October.
    2. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    3. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    4. Andersen, Torben G & Bollerslev, Tim, 1997. "Heterogeneous Information Arrivals and Return Volatility Dynamics: Uncovering the Long-Run in High Frequency Returns," Journal of Finance, American Finance Association, vol. 52(3), pages 975-1005, July.
    5. Sandeep Patel & Asani Sarkar, 1998. "Stock market crises in developed and emerging markets," Research Paper 9809, Federal Reserve Bank of New York.
    6. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    7. Bollerslev, Tim & Wright, Jonathan H., 2000. "Semiparametric estimation of long-memory volatility dependencies: The role of high-frequency data," Journal of Econometrics, Elsevier, vol. 98(1), pages 81-106, September.
    8. R. F. Engle & A. J. Patton, 2001. "What good is a volatility model?," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 237-245.
    9. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    10. Fujii, Eiji, 2002. "Exchange Rate and Price Adjustments in the Aftermath of the Asian Crisis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 7(1), pages 1-14, January.
    11. MacKinnon, James G, 1994. "Approximate Asymptotic Distribution Functions for Unit-Root and Cointegration Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(2), pages 167-176, April.
    12. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    13. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vo, Long H. & Roberts, Leigh, 2014. "On long memory behaviour and predictability of financial markets," Working Paper Series 3361, Victoria University of Wellington, School of Economics and Finance.
    2. Jonathan Dark, 2004. "Long memory in the volatility of the Australian All Ordinaries Index and the Share Price Index futures," Monash Econometrics and Business Statistics Working Papers 5/04, Monash University, Department of Econometrics and Business Statistics.
    3. Geoffrey Ngene & Ann Nduati Mungai & Allen K. Lynch, 2018. "Long-Term Dependency Structure and Structural Breaks: Evidence from the U.S. Sector Returns and Volatility," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 21(02), pages 1-38, June.
    4. Avishek Bhandari & Bandi Kamaiah, 2021. "Long Memory and Fractality Among Global Equity Markets: a Multivariate Wavelet Approach," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 23-37, March.
    5. Perron, Pierre & Qu, Zhongjun, 2010. "Long-Memory and Level Shifts in the Volatility of Stock Market Return Indices," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 275-290.
    6. Bhandari, Avishek, 2020. "Long Memory and Correlation Structures of Select Stock Returns Using Novel Wavelet and Fractal Connectivity Networks," MPRA Paper 101946, University Library of Munich, Germany.
    7. Bhandari, Avishek, 2020. "Long memory and fractality among global equity markets: A multivariate wavelet approach," MPRA Paper 99653, University Library of Munich, Germany.
    8. Kunal Saha & Vinodh Madhavan & Chandrashekhar G. R. & David McMillan, 2020. "Pitfalls in long memory research," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1733280-173, January.
    9. Eduardo Rossi, 2010. "Univariate GARCH models: a survey (in Russian)," Quantile, Quantile, issue 8, pages 1-67, July.
    10. Proietti, Tommaso, 2014. "Exponential Smoothing, Long Memory and Volatility Prediction," MPRA Paper 57230, University Library of Munich, Germany.
    11. Jean-Philippe Bouchaud & J. Doyne Farmer & Fabrizio Lillo, 2008. "How markets slowly digest changes in supply and demand," Papers 0809.0822, arXiv.org.
    12. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    13. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654.
    14. Evans, Kevin P. & Speight, Alan E.H., 2010. "Intraday periodicity, calendar and announcement effects in Euro exchange rate volatility," Research in International Business and Finance, Elsevier, vol. 24(1), pages 82-101, January.
    15. González-Pla, Francisco & Lovreta, Lidija, 2019. "Persistence in firm’s asset and equity volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    16. Jonathan Wright, 2002. "Log-Periodogram Estimation Of Long Memory Volatility Dependencies With Conditionally Heavy Tailed Returns," Econometric Reviews, Taylor & Francis Journals, vol. 21(4), pages 397-417.
    17. Long Hai Vo & Duc Hong Vo, 2019. "Application of Wavelet-Based Maximum Likelihood Estimator in Measuring Market Risk for Fossil Fuel," Sustainability, MDPI, vol. 11(10), pages 1-19, May.
    18. Assaf, A., 2006. "Dependence and mean reversion in stock prices: The case of the MENA region," Research in International Business and Finance, Elsevier, vol. 20(3), pages 286-304, September.
    19. Bollerslev, Tim, 2001. "Financial econometrics: Past developments and future challenges," Journal of Econometrics, Elsevier, vol. 100(1), pages 41-51, January.
    20. Bhardwaj, Geetesh & Swanson, Norman R., 2006. "An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 539-578.

    Corrections

    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:vuw:vuwecf:18828. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Library Technology Services (email available below). General contact details of provider: https://edirc.repec.org/data/egvuwnz.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.