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Impact of inventory-based electronic liquidity providers within a high-frequency event- and agent-based modeling framework

Author

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  • Alexandru Mandes

    (University of Giessen)

Abstract

This contribution addresses the impact of high-frequency electronic liquidity provision strategies on financial markets' intraday dynamics, by evaluating the interaction between multiple trading strategies within a computer laboratory, i.e. an artificial stock market. Initially, a realistic base-line model is set up around a continuous double auction market, with trading being pursued only by four types of low-frequency market participants. Sequentially, the high-frequency agents are added to the model and the corresponding changes related to various measures of market quality and market systemic risk are analyzed, under both regular and market stress conditions, such as when the order ow balance is suddenly disrupted by a large volume-in-line sell program. A detailed intraday analysis of a ash crash emergence is also conducted. Finally, possible regulatory policies such as minimum holding or quote resting time and financial-transaction taxes are assessed.

Suggested Citation

  • Alexandru Mandes, 2015. "Impact of inventory-based electronic liquidity providers within a high-frequency event- and agent-based modeling framework," MAGKS Papers on Economics 201515, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    • Handle: RePEc:mar:magkse:201515
    as

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    File URL: http://www.uni-marburg.de/fb02/makro/forschung/magkspapers/paper_2015/15-2015_mandes.pdf
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    References listed on IDEAS

    as
    1. Didier SORNETTE & Susanne VON DER BECKE, 2011. "Crashes and High Frequency Trading," Swiss Finance Institute Research Paper Series 11-63, Swiss Finance Institute.
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    3. Chiarella, Carl & Iori, Giulia, 2009. "The impact of heterogeneous trading rules on the limit order book and order flows," Journal of Economic Dynamics and Control, Elsevier, vol. 33(3), pages 525-537.
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    5. Lev Muchnik & Yoram Louzoun & Sorin Solomon, 2006. "Agent Based Simulation Design Principles — Applications to Stock Market," Springer Books, in: Hideki Takayasu (ed.), Practical Fruits of Econophysics, pages 183-188, Springer.
    6. Carl Chiarella & Giulia Iori, 2002. "A simulation analysis of the microstructure of double auction markets," Quantitative Finance, Taylor & Francis Journals, vol. 2(5), pages 346-353.
    7. Marco Avellaneda & Sasha Stoikov, 2008. "High-frequency trading in a limit order book," Quantitative Finance, Taylor & Francis Journals, vol. 8(3), pages 217-224.
    8. Didier SORNETTE & Susanne VON DER BECKE, 2011. "Crashes and High Frequency Trading," Swiss Finance Institute Research Paper Series 11-64, Swiss Finance Institute.
    9. David Easley & Marcos M. López de Prado & Maureen O'Hara, 2012. "Flow Toxicity and Liquidity in a High-frequency World," The Review of Financial Studies, Society for Financial Studies, vol. 25(5), pages 1457-1493.
    10. Gsell, Markus, 2008. "Assessing the impact of algorithmic trading on markets: A simulation approach," CFS Working Paper Series 2008/49, Center for Financial Studies (CFS).
    11. Alexandru Mandes, 2014. "Order Placement in a Continuous Double Auction Agent Based Model," MAGKS Papers on Economics 201443, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Donovan Platt & Tim Gebbie, 2016. "The Problem of Calibrating an Agent-Based Model of High-Frequency Trading," Papers 1606.01495, arXiv.org, revised Mar 2017.
    2. Platt, Donovan & Gebbie, Tim, 2018. "Can agent-based models probe market microstructure?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1092-1106.

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    More about this item

    Keywords

    agent-based modeling; continuous double auction; high-frequency trading; electronic liquidity provision; market quality; systemic risk; ash crash; regulatory policies;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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