IDEAS home Printed from https://ideas.repec.org/a/oup/jfinec/v6y2008i1p143-170.html
   My bibliography  Save this article

Modeling a Multivariate Transaction Process

Author

Listed:
  • Ingmar Nolte

Abstract

In this paper the dynamics of a joint transaction process are investigated. The transaction process is characterized by four marks: price changes, transaction volumes, bid--ask spreads and intertrade durations. Based on a copula approach, a model for their joint density is proposed, which avoids forcing a priori assumptions on the instantaneous causality relationships between the four variables as necessary in decomposition models, where the joint density is decomposed into its conditional and unconditional densities. The price change process is treated as a discrete process and specified with an integer count hurdle model and the transaction volumes, bid--ask spreads, and trade durations processes are modeled along the lines of fractionally integrated autoregressive conditional models, which are suited very well to capture the high persistency, empirically observed in these processes. The model is applied to three stocks traded at the New York Stock Exchange (NYSE) in May, 2001 and we investigate several market microstructure hypotheses in the empirical part of this paper. Copyright The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.

Suggested Citation

  • Ingmar Nolte, 2008. "Modeling a Multivariate Transaction Process," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 6(1), pages 143-170, Winter.
  • Handle: RePEc:oup:jfinec:v:6:y:2008:i:1:p:143-170
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/jjfinec/nbm020
    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.

    Citations

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


    Cited by:

    1. WeiƟ, Gregor N.F. & Supper, Hendrik, 2013. "Forecasting liquidity-adjusted intraday Value-at-Risk with vine copulas," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3334-3350.
    2. Gunther Wuyts, 2012. "The impact of aggressive orders in an order-driven market: a simulation approach," The European Journal of Finance, Taylor & Francis Journals, vol. 18(10), pages 1015-1038, November.

    More about this item

    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:oup:jfinec:v:6:y:2008:i:1:p:143-170. 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: (Oxford University Press) or (Christopher F. Baum). General contact details of provider: http://edirc.repec.org/data/sofieea.html .

    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 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.

    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.