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Price Discovery in Tick Time

Author

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  • Schotman, Peter C
  • Frijns, Bart
Abstract
In this Paper we propose a tick time model for dealer quote interactions using ultra-high-frequency data. This model includes duration functions to measure the time dependence of volatility as well as information asymmetry. In order to assess price discovery we define several measures in tick time. These measures can be aggregated to calendar time and we define a comparable measure to Hasbrouck (1995) information shares. In our empirical part we examine the Island and Instinet Electronic Communication Networks, and three wholesale market makers for 20 actively traded stocks with varying liquidity at Nasdaq. Our results include that volatility does not increase with the duration between quote updates, and that longer quote durations lead to lower price discovery. In terms of price discovery we find that ECNs tend to dominate the liquid stocks, whereas market makers dominate the less liquid stocks.

Suggested Citation

  • Schotman, Peter C & Frijns, Bart, 2004. "Price Discovery in Tick Time," CEPR Discussion Papers 4456, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:4456
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Marcelo Fernandes & Cristina M. Scherrer, 2018. "Price discovery in dual‐class shares across multiple markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(1), pages 129-155, January.
    2. Hurvich, Cliiford & Wang, Yi, 2006. "A Pure-Jump Transaction-Level Price Model Yielding Cointegration, Leverage, and Nonsynchronous Trading Effects," MPRA Paper 1413, University Library of Munich, Germany.
    3. Lei Wu & Qingbin Meng & Kuan Xu, 2015. "'Slow-burn' spillover and 'fast and furious' contagion: a study of international stock markets," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 933-958, June.
    4. Helmut Herwartz, 2006. "Econometric analysis of high frequency data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 89-104, March.
    5. Wu, Lei & Liu, Chunlin & Meng, Qingbin & Zeng, Hongchao, 2018. "Price discovery in China's inter-bank bond market," Pacific-Basin Finance Journal, Elsevier, vol. 48(C), pages 84-98.
    6. Ozturk, Sait R. & van der Wel, Michel & van Dijk, Dick, 2017. "Intraday price discovery in fragmented markets," Journal of Financial Markets, Elsevier, vol. 32(C), pages 28-48.
    7. Otsubo, Yoichi, 2014. "International cross-listing and price discovery under trading concentration in the domestic market: Evidence from Japanese shares," Journal of Empirical Finance, Elsevier, vol. 25(C), pages 36-51.
    8. Jahanshahloo, Hossein & Spokeviciute, Laima, 2021. "Time weighted price contribution," Finance Research Letters, Elsevier, vol. 43(C).
    9. Mizrach, Bruce & Neely, Christopher J., 2008. "Information shares in the US Treasury market," Journal of Banking & Finance, Elsevier, vol. 32(7), pages 1221-1233, July.
    10. Jondeau, Eric & Lahaye, Jérôme & Rockinger, Michael, 2015. "Estimating the price impact of trades in a high-frequency microstructure model with jumps," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 205-224.
    11. Gavious, Arieh & Kedar-Levy, Haim, 2013. "The speed of stock price discovery," Journal of Financial Intermediation, Elsevier, vol. 22(2), pages 245-258.
    12. Sait R. Ozturk & Michel van der Wel & Dick van Dijk, 2015. "Why do Pit-Hours outlive the Pit?," Tinbergen Institute Discussion Papers 15-082/III, Tinbergen Institute.
    13. Lei Wu & Kuan Xu & Qingbin Meng, 2021. "Information flow and price discovery dynamics," Review of Quantitative Finance and Accounting, Springer, vol. 56(1), pages 329-367, January.
    14. Kehrle, Kerstin & Peter, Franziska J., 2013. "Who moves first? An intensity-based measure for information flows across stock exchanges," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1629-1642.

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    More about this item

    Keywords

    Price discovery; Tick time models; Nasdaq; Ultra-high frequency data; Microstructure;
    All these keywords.

    JEL classification:

    • 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
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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