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Stock Data, Trade Durations, And Limit Order Book Information

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  • Simonsen, Ola

    () (Department of Economics, Umeå University)

Abstract

This thesis comprises four papers concerning trade durations and limit order book information. Paper [1], [2] and [4] study trader durations, e.g., the time between stock transactions in intra-day data. Paper [3] focus on the information content in the limit order book concerning future price movements in stock transaction data. Paper [1] 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 for intraday duration models. 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. Empirically, 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. Paper [2] considers an extension of the univariate autoregressive conditional duration model to which durations from a second stock are added. The model is empirically used to study duration dependence in four traded stocks, Nordea, Föreningssparbanken, Handelsbanken and SEB A on the Stockholm Stock Exchange. The stocks are all active in the banking sector. It is found that including durations from a second stock may add explanatory power to the univariate model. We also find that spread changes have significant effect for all series. Paper [3] empirically tests whether an open limit order book contains information about future short-run stock price movements. To account for the discrete nature of price changes, the integer-valued autoregressive model of order one is utilized. A model transformation has an advantage over conventional count data approaches since it handles negative integer-valued price changes. The empirical results reveal that measures capturing offered quantities of a share at the best bid- and ask-price reveal more information about future short-run price movements than measures capturing the quantities offered at prices below and above. Imbalance and changes in offered quantities at prices below and above the best bid- and askprice do, however, have a small and significant effect on future price changes. The results also indicate that the value of order book information is short-term. Paper [4] This paper studies the impact of news announcements on trade durations in stocks on the Stockholm Stock Exchange. The news are categorized into four groups and the impact on the time between transactions is studied. Times before, during and after the news release are considered. Econometrically, the impact is studied within an autoregressive conditional duration model using intradaily data for six stocks. The empirical results reveal that news reduces the duration lengths before, during and after news releases as expected by the theoretical litterature on durations and information flow.

Suggested Citation

  • Simonsen, Ola, 2006. "Stock Data, Trade Durations, And Limit Order Book Information," Umeå Economic Studies 689, Umeå University, Department of Economics.
  • Handle: RePEc:hhs:umnees:0689
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    References listed on IDEAS

    as
    1. Berry, Thomas D & Howe, Keith M, 1994. " Public Information Arrival," Journal of Finance, American Finance Association, vol. 49(4), pages 1331-1346, September.
    2. Bauwens, Luc & Ben Omrane, Walid & Giot, Pierre, 2005. "News announcements, market activity and volatility in the euro/dollar foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 24(7), pages 1108-1125, November.
    3. 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.
    4. 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.
    5. BAUWENS, Luc & GALLI, Fausto & GIOT, Pierre, 2003. "The moments of Log-ACD models," CORE Discussion Papers 2003011, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. Mitchell, Mark L & Mulherin, J Harold, 1994. " The Impact of Public Information on the Stock Market," Journal of Finance, American Finance Association, vol. 49(3), pages 923-950, July.
    7. Ederington, Louis H & Lee, Jae Ha, 1993. " How Markets Process Information: News Releases and Volatility," Journal of Finance, American Finance Association, vol. 48(4), pages 1161-1191, September.
    8. 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.
    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.
    10. Simonsen, Ola, 2005. "An Empirical Model for Durations in Stocks," Umeå Economic Studies 657, Umeå University, Department of Economics.
    11. 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.
    12. Kalev, Petko S. & Liu, Wai-Man & Pham, Peter K. & Jarnecic, Elvis, 2004. "Public information arrival and volatility of intraday stock returns," Journal of Banking & Finance, Elsevier, vol. 28(6), pages 1441-1467, June.
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    More about this item

    Keywords

    Finance; Maximum likelihood; Estimation; ACD; News; Multivariate; Intraday; Market microstructure; Granger causality; Time series; INAR; Stock price; Open limit order book;

    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
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • 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
    • 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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