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Impact of Electronic Liquidity Providers Within a High-Frequency Agent-Based Modeling Framework

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  • Alexandru Mandes

    (University of Giessen)

Abstract

The current contribution addresses the impact of high-frequency electronic liquidity provision strategies on the intraday dynamics of financial markets, by means of an artificial stock market. As novel design feature, an event-based intraday time implementation is proposed, allowing for the generation of time-stamped intraday events, which make possible both the aggregation of time series at various time frequencies, as well as the correct simulation of trading strategies that follow different temporal frequencies, e.g., low- and high-frequency. We provide new insights with respect to the determinants of extreme events, such as flash crashes. Finally, we compare the causal chains and the effectiveness of two potential regulatory policies under the same market circumstances, i.e., minimum resting time and financial-transaction taxes, not only with respect to their flash crash prevention power, but also regarding their impact on market participants and market quality, shedding new light on the policy trade-offs.

Suggested Citation

  • Alexandru Mandes, 2020. "Impact of Electronic Liquidity Providers Within a High-Frequency Agent-Based Modeling Framework," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 407-450, February.
    • Handle: RePEc:kap:compec:v:55:y:2020:i:2:d:10.1007_s10614-019-09891-1
    DOI: 10.1007/s10614-019-09891-1
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    References listed on IDEAS

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    1. Didier SORNETTE & Susanne VON DER BECKE, 2011. "Crashes and High Frequency Trading," Swiss Finance Institute Research Paper Series 11-63, Swiss Finance Institute.
    2. Herbert Dawid & Michael Neugart, 2011. "Agent-based Models for Economic Policy Design," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 37(1), pages 44-50.
    3. 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.
    4. repec:bla:jecsur:v:22:y:2008:i:4:p:711-751 is not listed on IDEAS
    5. Marco Avellaneda & Sasha Stoikov, 2008. "High-frequency trading in a limit order book," Quantitative Finance, Taylor & Francis Journals, vol. 8(3), pages 217-224.
    6. 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.
    7. Alexandru Mandes & Peter Winker, 2017. "Complexity and model comparison in agent based modeling of financial markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(3), pages 469-506, October.
    8. Herbert Dawid & Simon Gemkow & Philipp Harting & Michael Neugart, 2013. "Labor Market Integration Policies and The Convergence of Regions: The Role of Skills and Technology Diffusion," Economic Complexity and Evolution, in: Guido Buenstorf & Uwe Cantner & Horst Hanusch & Michael Hutter & Hans-Walter Lorenz & Fritz Rahmeyer (ed.), The Two Sides of Innovation, edition 127, pages 167-186, Springer.
    9. Jean-Philippe Bouchaud & Yuval Gefen & Marc Potters & Matthieu Wyart, 2004. "Fluctuations and response in financial markets: the subtle nature of 'random' price changes," Quantitative Finance, Taylor & Francis Journals, vol. 4(2), pages 176-190.
    10. Christophe Deissenberg & Sander van Der Hoog & Herbert Dawid, 2008. "EURACE: A Massively Parallel Agent-Based Model of the European Economy," Working Papers halshs-00339756, HAL.
    11. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2014. "High-Frequency Trading and Price Discovery," The Review of Financial Studies, Society for Financial Studies, vol. 27(8), pages 2267-2306.
    12. Didier SORNETTE & Susanne VON DER BECKE, 2011. "Crashes and High Frequency Trading," Swiss Finance Institute Research Paper Series 11-64, Swiss Finance Institute.
    13. Harris, Larry, 2002. "Trading and Exchanges: Market Microstructure for Practitioners," OUP Catalogue, Oxford University Press, number 9780195144703.
    14. 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.
    15. Westerhoff Frank H., 2008. "The Use of Agent-Based Financial Market Models to Test the Effectiveness of Regulatory Policies," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(2-3), pages 195-227, April.
    16. 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).
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    More about this item

    Keywords

    Agent-based modeling; High-frequency trading; Electronic liquidity provision; Market quality; Flash crash; Regulatory policy;
    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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