IDEAS home Printed from https://ideas.repec.org/a/eee/ecofin/v54y2020ics1062940820301376.html
   My bibliography  Save this article

How does HFT activity impact market volatility and the bid-ask spread after an exogenous shock? An empirical analysis on S&P 500 ETF

Author

Listed:
  • Bazzana, Flavio
  • Collini, Andrea

Abstract

In this paper, we empirically analyse infra-second datasets of the SPDR S&P 500 ETF (specifically, the ETF of the S&P 500 exchanged on BATS, named SPY.Z) in order to explain how high-frequency trading (HFT) activities (aggressive and passive) impact market volatility and the bid-ask spread before and after an exogenous shock (i.e., the 2016 US presidential election). Using SPDR S&P 500 ETF datasets as a proxy for the market on regular volume trading days (November 3, 2016) and on high-volume trading days (November 9, 2016), we show that HFT, on average, has a disturbing action mainly on regular volume trading days, whereas on high-volume trading days, it appears to have a stabilizing effect by balancing both the volatility and bid-ask spread. That is, HFT as a whole has a more neutral impact on the market’s volatility and bid-ask spread than the single aggressive and passive components. In fact, aggressive HFT has a consistent negative effect that increases, on average, both the volatility and bid-ask spread, whereas passive HFT displays a positive effect that decreases, on average, the volatility and bid-ask spread.

Suggested Citation

  • Bazzana, Flavio & Collini, Andrea, 2020. "How does HFT activity impact market volatility and the bid-ask spread after an exogenous shock? An empirical analysis on S&P 500 ETF," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
  • Handle: RePEc:eee:ecofin:v:54:y:2020:i:c:s1062940820301376
    DOI: 10.1016/j.najef.2020.101240
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1062940820301376
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.najef.2020.101240?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bank, Matthias & Baumann, Ralf H., 2016. "Price formation, market quality and the effects of reduced latency in the very short run," Research in International Business and Finance, Elsevier, vol. 37(C), pages 629-645.
    2. Aitken, Michael & Cumming, Douglas & Zhan, Feng, 2015. "High frequency trading and end-of-day price dislocation," Journal of Banking & Finance, Elsevier, vol. 59(C), pages 330-349.
    3. Kelejian, Harry H. & Mukerji, Purba, 2016. "Does high frequency algorithmic trading matter for non-AT investors?," Research in International Business and Finance, Elsevier, vol. 37(C), pages 78-92.
    4. Virgilio, Gianluca, 2017. "Is high-frequency trading tiering the financial markets?," Research in International Business and Finance, Elsevier, vol. 41(C), pages 158-171.
    5. Serbera, Jean-Philippe & Paumard, Pascal, 2016. "The fall of high-frequency trading: A survey of competition and profits," Research in International Business and Finance, Elsevier, vol. 36(C), pages 271-287.
    6. Alex Frino & Vito Mollica & Robert I. Webb, 2014. "The Impact of Co‐Location of Securities Exchanges' and Traders' Computer Servers on Market Liquidity," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 34(1), pages 20-33, January.
    7. Glantz, Morton & Kissell, Robert, 2013. "Multi-Asset Risk Modeling," Elsevier Monographs, Elsevier, edition 1, number 9780124016903.
    8. 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.
    9. Carrion, Allen, 2013. "Very fast money: High-frequency trading on the NASDAQ," Journal of Financial Markets, Elsevier, vol. 16(4), pages 680-711.
    10. Hasbrouck, Joel & Saar, Gideon, 2013. "Low-latency trading," Journal of Financial Markets, Elsevier, vol. 16(4), pages 646-679.
    11. Conrad, Jennifer & Wahal, Sunil & Xiang, Jin, 2015. "High-frequency quoting, trading, and the efficiency of prices," Journal of Financial Economics, Elsevier, vol. 116(2), pages 271-291.
    12. Manahov, Viktor & Hudson, Robert & Gebka, Bartosz, 2014. "Does high frequency trading affect technical analysis and market efficiency? And if so, how?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 28(C), pages 131-157.
    13. Hagströmer, Björn & Nordén, Lars, 2013. "The diversity of high-frequency traders," Journal of Financial Markets, Elsevier, vol. 16(4), pages 741-770.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Oguz Ersan & Montasser Ghachem, 2024. "Identifying Information Types in the Estimation of Informed Trading: An Improved Algorithm," JRFM, MDPI, vol. 17(9), pages 1-20, September.
    2. Kathrin Hellmuth & Christian Klingenberg, 2022. "Computing Black Scholes with Uncertain Volatility-A Machine Learning Approach," Papers 2202.07378, arXiv.org.
    3. Ke Meng & Shouhao Li, 2021. "The adaptive market hypothesis and high frequency trading," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-19, December.
    4. Ersan, Oguz & Simsir, Serif Aziz & Simsek, Koray D. & Hasan, Afan, 2021. "The speed of stock price adjustment to corporate announcements: Insights from Turkey," Emerging Markets Review, Elsevier, vol. 47(C).
    5. Ekinci, Cumhur & Ersan, Oğuz, 2022. "High-frequency trading and market quality: The case of a “slightly exposed” market," International Review of Financial Analysis, Elsevier, vol. 79(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ekinci, Cumhur & Ersan, Oguz, 2018. "A new approach for detecting high-frequency trading from order and trade data," Finance Research Letters, Elsevier, vol. 24(C), pages 313-320.
    2. Ligot, Stephanie & Gillet, Roland & Veryzhenko, Iryna, 2021. "Intraday volatility smile: Effects of fragmentation and high frequency trading on price efficiency," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    3. Aggarwal, Nidhi & Panchapagesan, Venkatesh & Thomas, Susan, 2023. "When is the order-to-trade ratio fee effective?," Journal of Financial Markets, Elsevier, vol. 62(C).
    4. Karkowska, Renata & Palczewski, Andrzej, 2023. "Does high-frequency trading actually improve market liquidity? A comparative study for selected models and measures," Research in International Business and Finance, Elsevier, vol. 64(C).
    5. Brogaard, Jonathan & Carrion, Allen & Moyaert, Thibaut & Riordan, Ryan & Shkilko, Andriy & Sokolov, Konstantin, 2018. "High frequency trading and extreme price movements," Journal of Financial Economics, Elsevier, vol. 128(2), pages 253-265.
    6. Phiri, Andrew, 2017. "Threshold convergence between the federal fund rate and South African equity returns around the colocation period," Business and Economic Horizons (BEH), Prague Development Center (PRADEC), vol. 13(1).
    7. Zhou, Hao & Kalev, Petko S., 2019. "Algorithmic and high frequency trading in Asia-Pacific, now and the future," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 186-207.
    8. Gerig, Austin & Michayluk, David, 2017. "Automated liquidity provision," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 1-13.
    9. Breedon, Francis & Chen, Louisa & Ranaldo, Angelo & Vause, Nicholas, 2023. "Judgment day: Algorithmic trading around the Swiss franc cap removal," Journal of International Economics, Elsevier, vol. 140(C).
    10. Gider, Jasmin & Schmickler, Simon & Westheide, Christian, 2019. "High-frequency trading and price informativeness," SAFE Working Paper Series 248, Leibniz Institute for Financial Research SAFE, revised 2019.
    11. Yang, Haijun & Ge, Hengshun & Luo, Ying, 2020. "The optimal bid-ask price strategies of high-frequency trading and the effect on market liquidity," Research in International Business and Finance, Elsevier, vol. 53(C).
    12. Frino, Alex & Mollica, Vito & Webb, Robert I. & Zhang, Shunquan, 2017. "The impact of latency sensitive trading on high frequency arbitrage opportunities," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 91-102.
    13. Kemme, David M. & McInish, Thomas H. & Zhang, Jiang, 2022. "Market fairness and efficiency: Evidence from the Tokyo Stock Exchange," Journal of Banking & Finance, Elsevier, vol. 134(C).
    14. Tian, Xiao & Do, Binh & Duong, Huu Nhan & Kalev, Petko S., 2015. "Liquidity provision and informed trading by individual investors," Pacific-Basin Finance Journal, Elsevier, vol. 35(PA), pages 143-162.
    15. Jasmin Gider & Simon N. M. Schmickler & Christian Westheide, 2021. "High-Frequency Trading and Price Informativeness," CRC TR 224 Discussion Paper Series crctr224_2021_257, University of Bonn and University of Mannheim, Germany.
    16. Ersan, Oguz & Simsir, Serif Aziz & Simsek, Koray D. & Hasan, Afan, 2021. "The speed of stock price adjustment to corporate announcements: Insights from Turkey," Emerging Markets Review, Elsevier, vol. 47(C).
    17. Cartea, Álvaro & Payne, Richard & Penalva, José & Tapia, Mikel, 2019. "Ultra-fast activity and intraday market quality," Journal of Banking & Finance, Elsevier, vol. 99(C), pages 157-181.
    18. Ramos, Henrique Pinto & Perlin, Marcelo Scherer, 2020. "Does algorithmic trading harm liquidity? Evidence from Brazil," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    19. Ekinci, Cumhur & Ersan, Oğuz, 2022. "High-frequency trading and market quality: The case of a “slightly exposed” market," International Review of Financial Analysis, Elsevier, vol. 79(C).
    20. Chordia, Tarun & Miao, Bin, 2020. "Market efficiency in real time: Evidence from low latency activity around earnings announcements," Journal of Accounting and Economics, Elsevier, vol. 70(2).

    More about this item

    Keywords

    High-frequency trading; Volatility; Bid-ask spread;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecofin:v:54:y:2020:i:c:s1062940820301376. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620163 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.