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Time and the price impact of a trade: A structural approach

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  • Grammig, Joachim G.
  • Theissen, Erik
  • Wünsche, Oliver

Abstract

We revisit the role of time in measuring the price impact of trades using a new empirical method that combines spread decomposition and dynamic duration modeling. Previous studies which have addressed the issue in a vector-autoregressive framework conclude that times when markets are most active are times when there is an increased presence of informed trading. Our empirical analysis based on recent European and U.S. data offers challenging new evidence. We find that as trade intensity increases, the informativeness of trades tends to decrease. This result is consistent with the predictions of Admati and Pfleiderer's (1988) rational expectations model, and also with models of dynamic trading like those proposed by Parlour (1998) and Foucault (1999). Our results cast doubt on the common wisdom that fast markets bear particularly high adverse selection risks for uninformed market participants.

Suggested Citation

  • Grammig, Joachim G. & Theissen, Erik & Wünsche, Oliver, 2011. "Time and the price impact of a trade: A structural approach," CFS Working Paper Series 2011/08, Center for Financial Studies (CFS).
    • Handle: RePEc:zbw:cfswop:201108
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    Cited by:

    1. Ryu, Doojin, 2016. "Considering all microstructure effects: The extension of a trade indicator model," Economics Letters, Elsevier, vol. 146(C), pages 107-110.
    2. Chung, Kee H. & Park, Seongkyu “Gilbert” & Ryu, Doojin, 2016. "Trade duration, informed trading, and option moneyness," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 395-411.
    3. Thomas Pöppe & Michael Aitken & Dirk Schiereck & Ingo Wiegand, 2016. "A PIN per day shows what news convey: the intraday probability of informed trading," Review of Quantitative Finance and Accounting, Springer, vol. 47(4), pages 1187-1220, November.
    4. H�lena Beltran-Lopez & Joachim Grammig & Albert J. Menkveld, 2012. "Limit order books and trade informativeness," The European Journal of Finance, Taylor & Francis Journals, vol. 18(9), pages 737-759, October.
    5. Ibrahim, Boulis Maher & Kalaitzoglou, Iordanis Angelos, 2016. "Why do carbon prices and price volatility change?," Journal of Banking & Finance, Elsevier, vol. 63(C), pages 76-94.

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    More about this item

    Keywords

    Price Impact of Trades; Trading Intensity; Dynamic Duration Models; Spread Decomposition Models; Adverse Selection Risk;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • 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

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