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Time and price impact of a trade: A structural approach

  • Grammig, Joachim
  • Theissen, Erik
  • Wuensche, Oliver

Dufour and Engle (2000) have shown that the duration between subsequent trade events carries informational content with respect to the evolution of the fundamental asset value. Their analysis supports the notion that no trade means no information derived from Easley and O'Hara's (1992) microstructure model. This paper revisits the role of time in measuring the price impact of trades using a structural model and provides challenging new evidence. For that purpose we extend Madhavan et al.'s (1997) model to account for time varying trading intensities. Our results confirm predictions from strategic trading models put forth by Parlour (1998) and Foucault (1999) in which short durations between trades are not related to the processing of private information. Instead, they are caused by strategic trading of impatient non-informed agents who use market orders more intensively when order book liquidity is high.

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Paper provided by University of Cologne, Centre for Financial Research (CFR) in its series CFR Working Papers with number 07-12.

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Date of creation: 2007
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Handle: RePEc:zbw:cfrwps:0712
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  1. Dufour, Alfonso & Engle, Robert F, 1999. "Time and the Price Impact of a Trade," University of California at San Diego, Economics Working Paper Series qt62c0h04j, Department of Economics, UC San Diego.
  2. Boehmer, Ekkehart & Grammig, Joachim & Theissen, Erik, 2007. "Estimating the probability of informed trading--does trade misclassification matter?," Journal of Financial Markets, Elsevier, vol. 10(1), pages 26-47, February.
  3. Ananth Madhavan & Matthew Richardson & Mark Roomans, 1996. "Why Do Security Prices Change? A Transaction-Level Analysis of NYSE Stocks," New York University, Leonard N. Stern School Finance Department Working Paper Seires 96-34, New York University, Leonard N. Stern School of Business-.
  4. Parlour, Christine A, 1998. "Price Dynamics in Limit Order Markets," Review of Financial Studies, Society for Financial Studies, vol. 11(4), pages 789-816.
  5. Robert F. Engle, 2000. "The Econometrics of Ultra-High Frequency Data," Econometrica, Econometric Society, vol. 68(1), pages 1-22, January.
  6. Fernandes, Marcelo & Grammig, Joachim, 2006. "A family of autoregressive conditional duration models," Journal of Econometrics, Elsevier, vol. 130(1), pages 1-23, January.
  7. Anat R. Admati, Paul Pfleiderer, 1988. "A Theory of Intraday Patterns: Volume and Price Variability," Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 3-40.
  8. Foucault, Thierry, 1999. "Order flow composition and trading costs in a dynamic limit order market1," Journal of Financial Markets, Elsevier, vol. 2(2), pages 99-134, May.
  9. Hasbrouck, Joel, 1991. " Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March.
  10. 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.
  11. Hee-Joon Ahn, 2001. "Limit Orders, Depth, and Volatility: Evidence from the Stock Exchange of Hong Kong," Journal of Finance, American Finance Association, vol. 56(2), pages 767-788, 04.
  12. 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.
  13. Lee, Charles M C & Ready, Mark J, 1991. " Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-46, June.
  14. 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.
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