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Discretized Time and Conditional Duration Modelling for Stock Transaction Data

Author

Listed:
  • Brännäs, Kurt

    (Department of Economics, Umeå University)

  • Simonsen, Ola

    (Department of Economics, Umeå University)

Abstract

The paper considers conditional duration models in which durations are in continuous time but measured in grouped or discretized form. This feature of recorded durations in combination with a frequently traded stock is expected to negatively influence the performance of conventional estimators. A few estimators that account for the discreteness are discussed and compared in a Monte Carlo experiment. An EM-algorithm accounting for the discrete data performs better than those which do not. Empirical results are reported for trading durations in Ericsson B at Stockholmsbörsen for a three-week period of July 2002. The incorporation of level variables for past trading is rejected in favour of change variables. This enables an interpretation in terms of news effects. No evidence of asymmetric responses to news about prices and spreads is found.

Suggested Citation

  • Brännäs, Kurt & Simonsen, Ola, 2003. "Discretized Time and Conditional Duration Modelling for Stock Transaction Data," Umeå Economic Studies 610, Umeå University, Department of Economics.
    • Handle: RePEc:hhs:umnees:0610
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    References listed on IDEAS

    as
    1. 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.
    2. Olivier Cappé & Randal Douc & Eric Moulines & Christian Robert, 2002. "On the Convergence of the Monte Carlo Maximum Likelihood Method for Latent Variable Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(4), pages 615-635, December.
    3. Lee, Lung-Fei, 1997. "Simulated maximum likelihood estimation of dynamic discrete choice statistical models some Monte Carlo results," Journal of Econometrics, Elsevier, vol. 82(1), pages 1-35.
    4. 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.
    5. Chiang & Min-Hsien;Fan, 2004. "On the Intradaily Relationship between Information Revelation and Trade Duration: The Evidence of MSCI Taiwan Futures," Computing in Economics and Finance 2004 119, Society for Computational Economics.
    6. Min-Hsien Chiang & Cheng-Hsiang Wang, 2004. "Intradaily relationship between information revelation and trading duration under market trends: the evidence of MSCI Taiwan stock index futures," Applied Economics Letters, Taylor & Francis Journals, vol. 11(8), pages 495-501.
    7. 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.
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    Cited by:

    1. Kurt Brannas & A. M. M. Shahiduzzaman Quoreshi, 2010. "Integer-valued moving average modelling of the number of transactions in stocks," Applied Financial Economics, Taylor & Francis Journals, vol. 20(18), pages 1429-1440.
    2. Simonsen, Ola, 2005. "An Empirical Model for Durations in Stocks," Umeå Economic Studies 657, Umeå University, Department of Economics.

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    More about this item

    Keywords

    Grouped data; Maximum likelihood; EM-algorithm; Estimation; Finance; News;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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