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High-frequency trading, stock volatility, and intraday crashes

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  • Ben Ammar, Imen
  • Hellara, Slaheddine

Abstract

We examine the effect of high-frequency trading (HFT) on the price volatility of Euronext-listed stocks. Under stable market conditions, greater HFT intensity is associated with decreased stock price volatility. However, during periods of intraday crashes, rapid interactions between HFT algorithms lead to high rates of order cancellations and simultaneous withdrawals of high-frequency traders from the limit order book. High-frequency traders submit aggressive orders during these periods and consume more liquidity than they provide, resulting in increased stock price volatility.

Suggested Citation

  • Ben Ammar, Imen & Hellara, Slaheddine, 2022. "High-frequency trading, stock volatility, and intraday crashes," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 337-344.
    • Handle: RePEc:eee:quaeco:v:84:y:2022:i:c:p:337-344
    DOI: 10.1016/j.qref.2022.03.004
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    1. Biais, Bruno & Foucault, Thierry & Moinas, Sophie, 2015. "Equilibrium fast trading," Journal of Financial Economics, Elsevier, vol. 116(2), pages 292-313.
    2. Breckenfelder, Johannes, 2024. "Competition among high-frequency traders and market quality," Journal of Economic Dynamics and Control, Elsevier, vol. 166(C).
    3. Hendershott, Terrence & Riordan, Ryan, 2013. "Algorithmic Trading and the Market for Liquidity," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(4), pages 1001-1024, August.
    4. Chung, Kee H. & Chuwonganant, Chairat, 2018. "Market volatility and stock returns: The role of liquidity providers," Journal of Financial Markets, Elsevier, vol. 37(C), pages 17-34.
    5. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    6. Ole E. Barndorff‐Nielsen & Neil Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280, May.
    7. Michael W. Brandt & Francis X. Diebold, 2006. "A No-Arbitrage Approach to Range-Based Estimation of Return Covariances and Correlations," The Journal of Business, University of Chicago Press, vol. 79(1), pages 61-74, January.
    8. Enrique Moral-Benito, 2013. "Likelihood-Based Estimation of Dynamic Panels With Predetermined Regressors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(4), pages 451-472, October.
    9. Hsiao, Cheng & Hashem Pesaran, M. & Kamil Tahmiscioglu, A., 2002. "Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods," Journal of Econometrics, Elsevier, vol. 109(1), pages 107-150, July.
    10. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    11. Terrence Hendershott & Charles M. Jones & Albert J. Menkveld, 2011. "Does Algorithmic Trading Improve Liquidity?," Journal of Finance, American Finance Association, vol. 66(1), pages 1-33, February.
    12. Hasbrouck, Joel & Saar, Gideon, 2013. "Low-latency trading," Journal of Financial Markets, Elsevier, vol. 16(4), pages 646-679.
    13. Robert A. Jarrow & Philip Protter, 2012. "A Dysfunctional Role Of High Frequency Trading In Electronic Markets," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 15(03), pages 1-15.
    14. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 2002. "Range‐Based Estimation of Stochastic Volatility Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1047-1091, June.
    15. Alain P. Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Clara Vega, 2014. "Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market," Journal of Finance, American Finance Association, vol. 69(5), pages 2045-2084, October.
    16. Torben G. Andersen & Viktor Todorov, 2009. "Realized Volatility and Multipower Variation," CREATES Research Papers 2009-49, Department of Economics and Business Economics, Aarhus University.
    17. Hansen, Peter R. & Lunde, Asger, 2006. "Realized Variance and Market Microstructure Noise," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 127-161, April.
    18. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
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    1. Olgun, Onur & Ekinci, Cumhur & Arıkan, Ramazan, 2024. "The performance of selected high-frequency trading proxies: An application on Turkish index futures market," Finance Research Letters, Elsevier, vol. 65(C).

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    More about this item

    Keywords

    Algorithmic trading; High-frequency trading; Market microstructure; Stock volatility; Intraday crashes;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G19 - Financial Economics - - General Financial Markets - - - Other

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