IDEAS home Printed from https://ideas.repec.org/a/kap/fmktpm/v21y2007i3p353-379.html
   My bibliography  Save this article

Price–volume relations of DAX companies

Author

Listed:
  • Henryk Gurgul

    ()

  • Paweł Majdosz

    ()

  • Roland Mestel

    ()

Abstract

No abstract is available for this item.

Suggested Citation

  • Henryk Gurgul & Paweł Majdosz & Roland Mestel, 2007. "Price–volume relations of DAX companies," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 21(3), pages 353-379, September.
  • Handle: RePEc:kap:fmktpm:v:21:y:2007:i:3:p:353-379
    DOI: 10.1007/s11408-007-0050-x
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11408-007-0050-x
    Download Restriction: Access to full text is restricted to subscribers.

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

    References listed on IDEAS

    as
    1. Liesenfeld, Roman, 1998. "Dynamic Bivariate Mixture Models: Modeling the Behavior of Prices and Trading Volume," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 101-109, January.
    2. Giampiero Gallo & Barbara Pacini, 2000. "The effects of trading activity on market volatility," The European Journal of Finance, Taylor & Francis Journals, vol. 6(2), pages 163-175.
    3. Bessembinder, Hendrik & Seguin, Paul J., 1993. "Price Volatility, Trading Volume, and Market Depth: Evidence from Futures Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 28(01), pages 21-39, March.
    4. Karpoff, Jonathan M., 1987. "The Relation between Price Changes and Trading Volume: A Survey," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(01), pages 109-126, March.
    5. Brunnermeier, Markus K., 2001. "Asset Pricing under Asymmetric Information: Bubbles, Crashes, Technical Analysis, and Herding," OUP Catalogue, Oxford University Press, number 9780198296980.
    6. M. F. Omran & E. McKenzie, 2000. "Heteroscedasticity in stock returns data revisited: volume versus GARCH effects," Applied Financial Economics, Taylor & Francis Journals, vol. 10(5), pages 553-560.
    7. Lamoureux, Christopher G & Lastrapes, William D, 1994. "Endogenous Trading Volume and Momentum in Stock-Return Volatility," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(2), pages 253-260, April.
    8. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
    9. Copeland, Thomas E, 1976. "A Model of Asset Trading under the Assumption of Sequential Information Arrival," Journal of Finance, American Finance Association, vol. 31(4), pages 1149-1168, September.
    10. Andersen, Torben G, 1996. " Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility," Journal of Finance, American Finance Association, vol. 51(1), pages 169-204, March.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Shmilovici Armin & Ben-Gal Irad, 2012. "Predicting Stock Returns Using a Variable Order Markov Tree Model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(5), pages 1-33, December.
    2. Henfryk Gurgul & lukasz Lach, 2009. "Linear versus nonlinear causalityfor dax companies," Operations Research and Decisions, Wroclaw University of Technology, Institute of Organization and Management, vol. 3, pages 27-46.

    More about this item

    Keywords

    DAX companies; Stock returns; Trading volume; Contemporaneous and dynamic relations; Tail dependencies; C32; G14;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    Statistics

    Access and download statistics

    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:kap:fmktpm:v:21:y:2007:i:3:p:353-379. 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: (Sonal Shukla) or (Rebekah McClure). General contact details of provider: http://www.springer.com .

    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.