IDEAS home Printed from https://ideas.repec.org/a/wsi/ijitdm/v05y2006i03ns0219622006002088.html
   My bibliography  Save this article

Long-Term Memory In Emerging Markets: Evidence From The Chinese Stock Market

Author

Listed:
  • CHAOQUN MA

    (Department of Management Science and Engineering, College of Business Administration, Hunan University, Hunan Province, P. R. China, 410082, P. R. China)

  • HONGQUAN LI

    (School of Business, Hunan Normal University, Changsha, Hunan 410081, China)

  • LIN ZOU

    (College of Business Administration, Hunan University, Changsha, Hunan 410082, China)

  • ZHIJIAN WU

    (Department of Mathematics, The University of Alabama, Tuscaloosa, AL 35487-0350, USA)

Abstract

The notion of long-term memory has received considerable attention in empirical finance. This paper makes two main contributions. First one is, the paper provides evidence of long-term memory dynamics in the equity market of China. An analysis of market patterns in the Chinese market (a typical emerging market) instead of US market (a developed market) will be meaningful because little research on the behaviors of emerging markets has been carried out previously. Second one is, we present a comprehensive research on the long-term memory characteristics in the Chinese stock market returns as well as volatilities. While many empirical results have been obtained on the detection of long-term memory in returns series, very few investigations are focused on the market volatility, though the long-term dependence in volatility may lead to some types of volatility persistence as observed in financial markets and affect volatility forecasts and derivative pricing formulas. By means of using modified rescaled range analysis and Autoregressive Fractally Integrated Moving Average model testing, this study examines the long-term dependence in Chinese stock market returns and volatility. The results show that although the returns themselves contain little serial correlation, the variability of returns has significant long-term dependence. It would be beneficial to encompass long-term memory structure to assess the behavior of stock prices and to research on financial market theory.

Suggested Citation

  • Chaoqun Ma & Hongquan Li & Lin Zou & Zhijian Wu, 2006. "Long-Term Memory In Emerging Markets: Evidence From The Chinese Stock Market," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 5(03), pages 495-501.
  • Handle: RePEc:wsi:ijitdm:v:05:y:2006:i:03:n:s0219622006002088
    DOI: 10.1142/S0219622006002088
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219622006002088
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1142/S0219622006002088?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Yang, Jian & Yu, Ziliang & Deng, Yongheng, 2018. "Housing price spillovers in China: A high-dimensional generalized VAR approach," Regional Science and Urban Economics, Elsevier, vol. 68(C), pages 98-114.
    2. Viorica Chirilă & Ciprian Chirilă, 2020. "Asymmetric Return and Volatility Transmission in Euro Zone and Baltic Countries Stock Markets," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 2-11, December.
    3. Pan, Shuiyang & Long, Suwan(Cheng) & Wang, Yiming & Xie, Ying, 2023. "Nonlinear asset pricing in Chinese stock market: A deep learning approach," International Review of Financial Analysis, Elsevier, vol. 87(C).
    4. Payal Jain & Sanjay Sehgal, 2019. "An examination of return and volatility spillovers between mature equity markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 43(1), pages 180-210, January.
    5. Ziliang Yu & Jian Yang & Robert I. Webb, 2023. "Price discovery in China's crude oil futures markets: An emerging Asian benchmark?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(3), pages 297-324, March.
    6. Anju Bala & Kapil Gupta, 2020. "Examining The Long Memory In Stock Returns And Liquidity In India," Copernican Journal of Finance & Accounting, Uniwersytet Mikolaja Kopernika, vol. 9(3), pages 25-43.

    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:wsi:ijitdm:v:05:y:2006:i:03:n:s0219622006002088. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijitdm/ijitdm.shtml .

    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.