Advanced Search
MyIDEAS: Login

Bivariate Integer-Valued Long Memory Model for High Frequency Financial Count Data

Contents:

Author Info

  • Quoreshi, A.M.M. Shahiduzzaman

    ()
    (CITR, Blekinge Inst of Technology)

Registered author(s):

    Abstract

    We develop a model to account for the long memory property in a bivariate count data framework. We propose a bivariate integer-valued fractional integrated (BINFIMA) model and apply the model to high frequency stock transaction data. The BINFIMA model allows for both positive and negative correlations between the counts. The unconditional and conditional first and second order moments are given. The CLS and FGLS estimators are discussed. The model is capable of capturing the covariance between and within intra-day time series of high frequency transaction data due to macroeconomic news and news related to a specific stock. Empirically, it is found that Ericsson B has mean recursive process while AstraZeneca has long memory property. It is also found that Ericsson B and AstraZenica react in a similar way due to macroeconomic news.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://www.bth.se/mam/forskning.nsf/attachments/WP%202014_3_Quoreshi_pdf/$file/WP%202014_3_Quoreshi.pdf
    Download Restriction: no

    Bibliographic Info

    Paper provided by Center for Innovation and Technology Research, Blekinge Institute of Technology in its series CITR Working Paper Series with number 2014/03.

    as in new window
    Length: 11 pages
    Date of creation: 02 Apr 2014
    Date of revision:
    Handle: RePEc:hhs:bthcsi:2014-003

    Contact details of provider:
    Postal: CITR (Center for Innovation and Technology Research), Department of Industrial Economics, Blekinge Inst of Technology, 371 79 Karlskrona, Sweden
    Phone: 0455 - 38 50 00
    Fax: 0455 - 38 50 57
    Web page: http://www.bth.se/csir
    More information through EDIRC

    Related research

    Keywords: Count data; Intra-day; Time series; Estimation; Reaction time; Finance;

    Find related papers by JEL classification:

    This paper has been announced in the following NEP Reports:

    References

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
    as in new window
    1. 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.
    2. Granger, Clive W. J. & Ding, Zhuanxin, 1996. "Varieties of long memory models," Journal of Econometrics, Elsevier, vol. 73(1), pages 61-77, July.
    3. Easley, David & O'Hara, Maureen, 1992. " Time and the Process of Security Price Adjustment," Journal of Finance, American Finance Association, vol. 47(2), pages 576-605, June.
    4. Robert F. Engle, 1996. "The Econometrics of Ultra-High Frequency Data," NBER Working Papers 5816, National Bureau of Economic Research, Inc.
    5. Quoreshi, Shahiduzzaman, 2006. "LongMemory, Count Data, Time Series Modelling for Financial Application," UmeÃ¥ Economic Studies 673, Umeå University, Department of Economics.
    6. Brännäs, Kurt & Quoreshi, Shahiduzzaman, 2004. "Integer-Valued Moving Average Modelling of the Number of Transactions in Stocks," UmeÃ¥ Economic Studies 637, Umeå University, Department of Economics.
    7. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
    8. Quoreshi, Shahiduzzaman, 2005. "Bivariate Time Series Modelling of Financial Count Data," UmeÃ¥ Economic Studies 655, Umeå University, Department of Economics.
    9. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:hhs:bthcsi:2014-003. 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: (Sam Tavassoli).

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 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.