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Liquidity Dynamics in an Electronic Open Limit Order Book: an Event Study Approach

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  • Peter Gomber
  • Uwe Schweickert
  • Erik Theissen

Abstract

We analyse the dynamics of liquidity in an electronic limit order book using the Exchange Liquidity Measure (XLM), a measure of the cost of a roundtrip trade of given size V. We use intraday event study methodology to analyse how liquidity shocks †large transactions and Bloomberg ticker news †affect the XLM. We find that resiliency after large transactions is high, i.e., liquidity quickly reverts to ‘normal’ levels. Large trades are ‘timed’; they take place at times when liquidity is unusually high. Bloomberg ticker news items do not have a discernible effect on liquidity.

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  • Peter Gomber & Uwe Schweickert & Erik Theissen, 2015. "Liquidity Dynamics in an Electronic Open Limit Order Book: an Event Study Approach," European Financial Management, European Financial Management Association, vol. 21(1), pages 52-78, January.
  • Handle: RePEc:bla:eufman:v:21:y:2015:i:1:p:52-78
    DOI: 10.1111/j.1468-036X.2013.12006.x
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    Cited by:

    1. Marvin Wee & Joey W. Yang, 2016. "The Evolution of Informed Liquidity Provision: Evidence from an Order†driven Market," European Financial Management, European Financial Management Association, vol. 22(5), pages 882-915, November.
    2. Pelizzon, Loriana & Sagade, Satchit & Vozian, Katia, 2020. "Resiliency: Cross-venue dynamics with Hawkes processes," SAFE Working Paper Series 291, Leibniz Institute for Financial Research SAFE.
    3. Siikanen, Milla & Kanniainen, Juho & Luoma, Arto, 2017. "What drives the sensitivity of limit order books to company announcement arrivals?," Economics Letters, Elsevier, vol. 159(C), pages 65-68.
    4. Martin Angerer & Georg Peter & Sebastian Stoeckl & Thomas Wachter & Matthias Bank & Marco Menichetti, 2018. "Bid-Ask Spread Patterns and the Optimal Timing for Discretionary Liquidity Traders on Xetra," Schmalenbach Business Review, Springer;Schmalenbach-Gesellschaft, vol. 70(3), pages 209-230, July.
    5. Siikanen, Milla & Kanniainen, Juho & Valli, Jaakko, 2017. "Limit order books and liquidity around scheduled and non-scheduled announcements: Empirical evidence from NASDAQ Nordic," Finance Research Letters, Elsevier, vol. 21(C), pages 264-271.
    6. Schnaubelt, Matthias, 2020. "Deep reinforcement learning for the optimal placement of cryptocurrency limit orders," FAU Discussion Papers in Economics 05/2020, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    7. Thomas A. P. de Boer & Cornelis Gardebroek & Joost M. E. Pennings & Andres Trujillo‐Barrera, 2022. "Intraday liquidity in soybean complex futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(7), pages 1189-1211, July.
    8. Matthias Schnaubelt & Jonas Rende & Christopher Krauss, 2019. "Testing Stylized Facts of Bitcoin Limit Order Books," JRFM, MDPI, vol. 12(1), pages 1-30, February.
    9. Jeon, Yoontae & Samarbakhsh, Laleh & Hewitt, Kenji, 2021. "Fragmentation in the Bitcoin market: Evidence from multiple coexisting order books," Finance Research Letters, Elsevier, vol. 39(C).
    10. Olaf Korn & Paolo Krischak & Erik Theissen, 2019. "Illiquidity transmission from spot to futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(10), pages 1228-1249, October.
    11. Olbrys, Joanna & Mursztyn, Michal, 2019. "Estimation of intraday stock market resiliency: Short-Time Fourier Transform approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    12. Benjamin Clapham & Martin Haferkorn & Kai Zimmermann, 2020. "Does Speed Matter? The Role Of High‐Frequency Trading For Order Book Resiliency," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 43(4), pages 933-964, December.
    13. O’Sullivan, Conall & Papavassiliou, Vassilios G. & Wafula, Ronald Wekesa & Boubaker, Sabri, 2024. "New insights into liquidity resiliency," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
    14. Olbrys, Joanna & Mursztyn, Michal, 2019. "Measuring stock market resiliency with Discrete Fourier Transform for high frequency data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 248-256.
    15. Fullwood, Jonathan & Massacci, Daniele, 2018. "Liquidity resilience in the UK gilt futures market: evidence from the order book," Bank of England working papers 744, Bank of England.
    16. Ying Chen & Wee Song Chua & Wolfgang Karl Härdle, 2019. "Forecasting limit order book liquidity supply–demand curves with functional autoregressive dynamics," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1473-1489, September.
    17. Schnaubelt, Matthias, 2022. "Deep reinforcement learning for the optimal placement of cryptocurrency limit orders," European Journal of Operational Research, Elsevier, vol. 296(3), pages 993-1006.
    18. Daniel Havran & Kata Varadi, 2015. "Price Impact and the Recovery of the Limit Order Book: Why Should We Care About Informed Liquidity Providers?," CERS-IE WORKING PAPERS 1540, Institute of Economics, Centre for Economic and Regional Studies.
    19. repec:hum:wpaper:sfb649dp2016-025 is not listed on IDEAS
    20. Hai-Chuan Xu & Wei Chen & Xiong Xiong & Wei Zhang & Wei-Xing Zhou & H Eugene Stanley, 2016. "Limit-order book resiliency after effective market orders: Spread, depth and intensity," Papers 1602.00731, arXiv.org, revised Feb 2017.

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

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