IDEAS home Printed from https://ideas.repec.org/a/eee/beexfi/v37y2023ics2214635022001009.html
   My bibliography  Save this article

Do algorithmic traders exploit volatility?

Author

Listed:
  • Arumugam, Devika
  • Prasanna, P. Krishna
  • Marathe, Rahul R.

Abstract

This study examines the impact of trading by Algorithmic Traders (ATs) and Non-Algorithmic Traders (NATs) on volatility, and conversely, the impact of volatility shocks on ATs and Non-ATs. ATs are classified as High-Frequency Traders (HFTs) and Buy-side Algorithmic Traders (BATs). Using jump robust volatility estimates, we find that excessive directional and non-directional trading by BATs and HFTs increases volatility, whereas that by NATs marginally decreases volatility. Conversely, all traders increase their non-directional trading one hour following a volatility shock. BATs carry out more directional trades during a volatility shock, whereas HFTs withdraw from such activities.

Suggested Citation

  • Arumugam, Devika & Prasanna, P. Krishna & Marathe, Rahul R., 2023. "Do algorithmic traders exploit volatility?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    • Handle: RePEc:eee:beexfi:v:37:y:2023:i:c:s2214635022001009
    DOI: 10.1016/j.jbef.2022.100778
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2214635022001009
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.jbef.2022.100778?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
    ---><---

    References listed on IDEAS

    as
    1. De Long, J Bradford, et al, 1990. "Positive Feedback Investment Strategies and Destabilizing Rational Speculation," Journal of Finance, American Finance Association, vol. 45(2), pages 379-395, June.
    2. Aït-Sahalia, Yacine & Brunetti, Celso, 2020. "High frequency traders and the price process," Journal of Econometrics, Elsevier, vol. 217(1), pages 20-45.
    3. Andersen, Torben G. & Dobrev, Dobrislav & Schaumburg, Ernst, 2012. "Jump-robust volatility estimation using nearest neighbor truncation," Journal of Econometrics, Elsevier, vol. 169(1), pages 75-93.
    4. Pamela Saliba, 2020. "The information content of high-frequency traders aggressive orders: recent evidence," Quantitative Finance, Taylor & Francis Journals, vol. 20(11), pages 1779-1794, November.
    5. Arumugam, Devika & Krishna Prasanna, P., 2021. "Commonality and contrarian trading among algorithmic traders," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    6. Fung, Joseph K.W. & Lien, Donald & Tse, Yiuman & Tse, Yiu Kuen, 2005. "Effects of electronic trading on the Hang Seng Index futures market," International Review of Economics & Finance, Elsevier, vol. 14(4), pages 415-425.
    7. Benos, Evangelos & Brugler, James & Hjalmarsson, Erik & Zikes, Filip, 2017. "Interactions among High-Frequency Traders," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(4), pages 1375-1402, August.
    8. Scholtus, Martin & van Dijk, Dick & Frijns, Bart, 2014. "Speed, algorithmic trading, and market quality around macroeconomic news announcements," Journal of Banking & Finance, Elsevier, vol. 38(C), pages 89-105.
    9. Chen, Wei-Peng & Chung, Huimin & Lien, Donald, 2016. "Price discovery in the S&P 500 index derivatives markets," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 438-452.
    10. Malceniece, Laura & Malcenieks, Kārlis & Putniņš, Tālis J., 2019. "High frequency trading and comovement in financial markets," Journal of Financial Economics, Elsevier, vol. 134(2), pages 381-399.
    11. 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).
    12. Attila Ceffer & Norbert Fogarasi & Janos Levendovszky, 2018. "Trading by estimating the quantized forward distribution," Applied Economics, Taylor & Francis Journals, vol. 50(59), pages 6397-6405, December.
    13. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    14. Devika Arumugam & P Krishna Prasanna, 2021. "A Game of Hide-and-Seek between Proprietary and Buy-Side Algorithmic Traders: Causal links with Market Quality," Applied Economics, Taylor & Francis Journals, vol. 53(41), pages 4788-4798, September.
    15. Hasbrouck, Joel & Saar, Gideon, 2013. "Low-latency trading," Journal of Financial Markets, Elsevier, vol. 16(4), pages 646-679.
    16. 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.
    17. Philippe Casgrain & Sebastian Jaimungal, 2020. "Mean‐field games with differing beliefs for algorithmic trading," Mathematical Finance, Wiley Blackwell, vol. 30(3), pages 995-1034, July.
    18. Álvaro Cartea & José Penalva, 2012. "Where is the Value in High Frequency Trading?," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 2(03), pages 1-46.
    19. Jain, Pankaj K. & Jain, Pawan & McInish, Thomas H., 2016. "Does high-frequency trading increase systemic risk?," Journal of Financial Markets, Elsevier, vol. 31(C), pages 1-24.
    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. Arumugam, Devika, 2023. "Algorithmic trading: Intraday profitability and trading behavior," Economic Modelling, Elsevier, vol. 128(C).
    2. Dunbar, Kwamie & Owusu-Amoako, Johnson, 2023. "Predictability of crypto returns: The impact of trading behavior," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(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. 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).
    2. Arumugam, Devika & Krishna Prasanna, P., 2021. "Commonality and contrarian trading among algorithmic traders," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    3. Dodd, Olga & Frijns, Bart & Indriawan, Ivan & Pascual, Roberto, 2023. "US cross-listing and domestic high-frequency trading: Evidence from Canadian stocks," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 301-320.
    4. Arumugam, Devika, 2023. "Algorithmic trading: Intraday profitability and trading behavior," Economic Modelling, Elsevier, vol. 128(C).
    5. 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.
    6. Anagnostidis, Panagiotis & Fontaine, Patrice, 2020. "Liquidity commonality and high frequency trading: Evidence from the French stock market," International Review of Financial Analysis, Elsevier, vol. 69(C).
    7. 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).
    8. Tian, Xiao & Duong, Huu Nhan & Kalev, Petko S., 2019. "Information content of the limit order book for crude oil futures price volatility," Energy Economics, Elsevier, vol. 81(C), pages 584-597.
    9. Sánchez Serrano Antonio, 2020. "High-Frequency Trading and Systemic Risk: A Structured Review of Findings and Policies," Review of Economics, De Gruyter, vol. 71(3), pages 169-195, December.
    10. Hautsch, Nikolaus & Noé, Michael & Zhang, S. Sarah, 2017. "The ambivalent role of high-frequency trading in turbulent market periods," CFS Working Paper Series 580, Center for Financial Studies (CFS).
    11. Gerig, Austin & Michayluk, David, 2017. "Automated liquidity provision," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 1-13.
    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. 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).
    14. 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).
    15. 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.
    16. Roşu, Ioanid, 2019. "Fast and slow informed trading," Journal of Financial Markets, Elsevier, vol. 43(C), pages 1-30.
    17. Menkveld, Albert J., 2013. "High frequency trading and the new market makers," Journal of Financial Markets, Elsevier, vol. 16(4), pages 712-740.
    18. Jurich, Stephen N. & Mishra, Ajay Kumar & Parikh, Bhavik, 2020. "Indecisive algos: Do limit order revisions increase market load?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 28(C).
    19. Fabrice Rousseau & Herve Boco & Laurent Germain, 2020. "High Frequency Trading: Strategic Competition Between Slow and Fast Traders," Economics Department Working Paper Series n296-20.pdf, Department of Economics, National University of Ireland - Maynooth.
    20. Cox, Justin & Woods, Donovan, 2023. "COVID-19 and market structure dynamics," Journal of Banking & Finance, Elsevier, vol. 147(C).

    More about this item

    Keywords

    Algorithmic trading; High-frequency trading; Volatility;
    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
    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G40 - Financial Economics - - Behavioral Finance - - - General

    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:beexfi:v:37:y:2023:i:c:s2214635022001009. 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: https://www.journals.elsevier.com/journal-of-behavioral-and-experimental-finance .

    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.