IDEAS home Printed from https://ideas.repec.org/a/eee/econom/v141y2007i2p876-912.html
   My bibliography  Save this article

Modelling security market events in continuous time: Intensity based, multivariate point process models

Author

Listed:
  • Bowsher, Clive G.

Abstract

A continuous time econometric modelling framework for multivariate financial market event (or `transactions') data is developed in which the model is specified via the vector stochastic intensity. This has the advantage that the conditioning sigma-field is updated continuously in time as new information arrives. The class of generalised Hawkes models is introduced which allows the estimation of the dependence of the intensity on the events of previous trading days. Analytic likelihoods are available and it is shown how to construct diagnostic tests based on the transformation of non-Poisson processes into standard Poisson processes using random changes of time. A proof of the validity of the diagnostic testing procedures is given that imposes only a very weak condition on the point process model, thus establishing their widespread applicability. A continuous time, bivariate point process model of the timing of trades and mid-quote changes is presented for a New York Stock Exchange stock and the empirical findings are related to the theoretical and empirical market microstructure literature. The two-way interaction of trades and quote changes is found to be important empirically.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Bowsher, Clive G., 2007. "Modelling security market events in continuous time: Intensity based, multivariate point process models," Journal of Econometrics, Elsevier, vol. 141(2), pages 876-912, December.
  • Handle: RePEc:eee:econom:v:141:y:2007:i:2:p:876-912
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304-4076(06)00251-X
    Download Restriction: Full text for ScienceDirect subscribers only

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

    Other versions of this item:

    References listed on IDEAS

    as
    1. Francis X. Diebold & Todd A. Gunther & Anthony S. Tay, "undated". "Evaluating Density Forecasts," CARESS Working Papres 97-18, University of Pennsylvania Center for Analytic Research and Economics in the Social Sciences.
    2. BAUWENS, Luc & HAUTSCH, Nikolaus, 2003. "Dynamic latent factor models for intensity processes," CORE Discussion Papers 2003103, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Jeffrey R. Russell & Robert F. Engle, 1998. "Econometric Analysis of Discrete-valued Irregularly-spaced Financial Transactions Data Using a New Autoregressive Conditional Multinomial Model," CRSP working papers 470, Center for Research in Security Prices, Graduate School of Business, University of Chicago.
    4. 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.
    5. James D. Hamilton & Oscar Jorda, 2002. "A Model of the Federal Funds Rate Target," Journal of Political Economy, University of Chicago Press, vol. 110(5), pages 1135-1167, October.
    6. Alfonso Dufour & Robert F. Engle, 2000. "Time and the Price Impact of a Trade," Journal of Finance, American Finance Association, vol. 55(6), pages 2467-2498, December.
    7. Bowsher, Clive G., 2007. "Modelling security market events in continuous time: Intensity based, multivariate point process models," Journal of Econometrics, Elsevier, vol. 141(2), pages 876-912, December.
    8. Grammig, Joachim & Wellner, Marc, 2002. "Modeling the interdependence of volatility and inter-transaction duration processes," Journal of Econometrics, Elsevier, vol. 106(2), pages 369-400, February.
    9. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 361-393.
    10. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446.
    11. Easley, David & O'Hara, Maureen, 1987. "Price, trade size, and information in securities markets," Journal of Financial Economics, Elsevier, vol. 19(1), pages 69-90, September.
    12. Yongmiao Hong, 2005. "Nonparametric Specification Testing for Continuous-Time Models with Applications to Term Structure of Interest Rates," Review of Financial Studies, Society for Financial Studies, vol. 18(1), pages 37-84.
    13. Joel Hasbrouck, 1999. "Trading Fast and Slow: Security Market Events in Real Time," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-012, New York University, Leonard N. Stern School of Business-.
    14. Robert F. Engle & Asger Lunde, 2003. "Trades and Quotes: A Bivariate Point Process," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 1(2), pages 159-188.
    15. Anthony D. Hall & Nikolaus Hautsch, 2004. "A Continuous-Time Measurement of the Buy-Sell Pressure in a Limit Order Book Market," FRU Working Papers 2004/03, University of Copenhagen. Department of Economics. Finance Research Unit.
    16. Luc Bauwens & Nikolaus Hautsch, 2006. "Stochastic Conditional Intensity Processes," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(3), pages 450-493.
    17. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
    18. P. Frevert, 1971. "Note," Review of Economic Studies, Oxford University Press, vol. 38(2), pages 269-270.
    19. Spierdijk, L. & Nijman, T.E. & van Soest, A.H.O., 2002. "Modeling Comovements in Trading Intensities to Distinguish Sector and Stock Specific News," Discussion Paper 2002-69, Tilburg University, Center for Economic Research.
    20. Glosten, Lawrence R. & Milgrom, Paul R., 1985. "Bid, ask and transaction prices in a specialist market with heterogeneously informed traders," Journal of Financial Economics, Elsevier, vol. 14(1), pages 71-100, March.
    21. Andrews, Donald W K, 2001. "Testing When a Parameter Is on the Boundary of the Maintained Hypothesis," Econometrica, Econometric Society, vol. 69(3), pages 683-734, May.
    22. Engle, Robert F. & Russell, Jeffrey R., 1997. "Forecasting the frequency of changes in quoted foreign exchange prices with the autoregressive conditional duration model," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 187-212, June.
    23. Hall, Anthony D. & Hautsch, Nikolaus, 2007. "Modelling the buy and sell intensity in a limit order book market," Journal of Financial Markets, Elsevier, vol. 10(3), pages 249-286, August.
    24. 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.
    25. Arjas, Elja & Haara, Pentti, 1988. "A note on the exponentiality of total hazards before failure," Journal of Multivariate Analysis, Elsevier, vol. 26(2), pages 207-218, August.
    26. Rombouts, Jeroen V. K. & Bauwens, Luc, 2004. "Econometrics," Papers 2004,33, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
      • BAUWENS, Luc & ROMBOUTS, Jeroen V.K., 2004. "Econometrics," CORE Discussion Papers RP 1713, Universit√© catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    27. Lee, Charles M C & Ready, Mark J, 1991. " Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-746, June.
    Full references (including those not matched with items on IDEAS)

    More about this item

    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

    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:eee:econom:v:141:y:2007:i:2:p:876-912. 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: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/jeconom .

    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.