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Liquidity dynamics in an electronic open limit order book: An event study approach

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  • Gomber, Peter
  • Schweickert, Uwe
  • Theissen, Erik

Abstract

We analyze the dynamics of liquidity in Xetra, an electronic open limit order book. We use the Exchange Liquidity Measure (XLM), a measure of the cost of a roundtrip trade of given size V. This measure captures the price and the quantity dimension of liquidity. We present descriptive statistics, analyze the cross-sectional determinants of the XLM measure and document its intraday pattern. Our main contribution is an analysis of the dynamics of the XLM measure around liquidity shocks. We use intraday event study methodology to analyze how a shock affects the XLM measure. We consider two sets of liquidity shocks, large transactions (which are endogenous events because they originate in the market) and Bloomberg ticker news items (which are exogenous events because they originate outside of the market). We find that resiliency after large transactions is high, i.e., liquidity quickly reverts to normal levels. We further document that large trades take place at times when liquidity is unusually high. We interpret this as evidence that large transactions are timed. The Bloomberg ticker news items do not have a discernible effect on liquidity.

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  • Gomber, Peter & Schweickert, Uwe & Theissen, Erik, 2011. "Liquidity dynamics in an electronic open limit order book: An event study approach," CFR Working Papers 11-14, University of Cologne, Centre for Financial Research (CFR).
    • Handle: RePEc:zbw:cfrwps:1114
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    Cited by:

    1. 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.
    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. Matthias Schnaubelt & Jonas Rende & Christopher Krauss, 2019. "Testing Stylized Facts of Bitcoin Limit Order Books," JRFM, MDPI, vol. 12(1), pages 1-30, February.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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).
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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).
    17. 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.
    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.

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    More about this item

    Keywords

    liquidity; limit order book; resiliency;
    All these keywords.

    JEL classification:

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

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