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Long Memory on the German Stock Exchange

Author

Listed:
  • Henryk GURGUL

    (Faculty of Management, University of Science and Technology, Krakow – corresponding author)

  • Tomasz WÓJTOWICZ

    (Faculty of Management, University of Science and Technology, Krakow)

Abstract

In this study, the contributors present the results of their investigations into the long-memory properties of trading volume and the volatility of stock returns (given by absolute returns and alternatively by square returns). Their database is daily stock data of German companies in the DAX segment of the German Stock Exchange. The purpose of these investigations is the calculation of memory parameters and to determine whether there exists the same degree of long memory for trading-volume and return-volatility data. Calculations are performed on daily results from January 1994 to November 2005 and in three sub-periods: January 1994 to December 1997, January 1998 to December 2001, and January 2002 to November 2005.

Suggested Citation

  • Henryk GURGUL & Tomasz WÓJTOWICZ, 2006. "Long Memory on the German Stock Exchange," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 56(09-10), pages 447-468, September.
    • Handle: RePEc:fau:fauart:v:56:y:2006:i:9-10:p:447-468
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Richards, Daniel W. & Willows, Gizelle D., 2019. "Monday mornings: Individual investor trading on days of the week and times within a day," Journal of Behavioral and Experimental Finance, Elsevier, vol. 22(C), pages 105-115.
    3. Kuswanto, Heri, 2009. "A New Simple Test Against Spurious Long Memory Using Temporal Aggregation," Hannover Economic Papers (HEP) dp-425, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    4. Hiremath, Gourishankar S & Bandi, Kamaiah, 2010. "Long Memory in Stock Market Volatility:Evidence from India," MPRA Paper 48519, University Library of Munich, Germany.
    5. Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2023. "A Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1801-1843, December.
    6. Henryk Gurgul & Lukaz Lach & Tomasz Wojtowicz, 2016. "Impact of US Macroeconomic News Announcements on Intraday Causalities on Selected European Stock Markets," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(5), pages 405-425, October.

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    More about this item

    Keywords

    DAX 30; trading volume; univariate and bivariate long memory;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G35 - Financial Economics - - Corporate Finance and Governance - - - Payout Policy

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