Discretized Time and Conditional Duration Modelling for Stock Transaction Data
AbstractThe 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.
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Bibliographic InfoPaper provided by Umeå University, Department of Economics in its series Umeå Economic Studies with number 610.
Length: 28 pages
Date of creation: 20 May 2003
Date of revision:
Publication status: Published in Applied Financial Economics, 2007, pages 647-658.
Contact details of provider:
Postal: Department of Economics, Umeå University, S-901 87 Umeå, Sweden
Phone: 090 - 786 61 42
Fax: 090 - 77 23 02
Web page: http://www.econ.umu.se/
More information through EDIRC
Grouped data; Maximum likelihood; EM-algorithm; Estimation; Finance; News;
Other versions of this item:
- Kurt Brannas & Ola Simonsen, 2007. "Discretized time and conditional duration modelling for stock transaction data," Applied Financial Economics, Taylor and Francis Journals, vol. 17(8), pages 647-658.
- 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
- 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
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies
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- 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.
- 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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94-06, Michigan - Center for Research on Economic & Social Theory.
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- Kurt Brannas & A. M. M. Shahiduzzaman Quoreshi, 2010.
"Integer-valued moving average modelling of the number of transactions in stocks,"
Applied Financial Economics,
Taylor and Francis Journals, vol. 20(18), pages 1429-1440.
- 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.
- Simonsen, Ola, 2005. "An Empirical Model for Durations in Stocks," UmeÃ¥ Economic Studies 657, Umeå University, Department of Economics.
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