IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/118914.html
   My bibliography  Save this paper

Volatility, dark trading and market quality: evidence from the 2020 COVID-19 pandemic-driven market volatility

Author

Listed:
  • Ibikunle, Gbenga
  • Rzayev, Khaladdin

Abstract

We exploit the exogenous shock of the COVID-19 pandemic on financial markets and regulatory restrictions on dark trading to investigate how volatility drives dark market share and trader venue selection. We find that, consistent with theory, excessive volatility on lit exchanges is linked with an economically significant loss of market share by dark pools to lit exchanges. The dynamics of market share loss are driven by the cross-migration of informed and uninformed traders between lit and dark venues. Informed traders migrate from lit venues to dark venues when lit venues' volatility becomes excessive, while uninformed traders, wary of the presence of informed traders in dark pools, shift their trading to lit exchanges rather than delay trading in a volatile market environment. The market quality implications of the cross-migration are mixed: while it improves liquidity on the lit exchange, it results in a loss of informational efficiency.

Suggested Citation

  • Ibikunle, Gbenga & Rzayev, Khaladdin, 2020. "Volatility, dark trading and market quality: evidence from the 2020 COVID-19 pandemic-driven market volatility," LSE Research Online Documents on Economics 118914, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:118914
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/118914/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    Full references (including those not matched with items on IDEAS)

    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. Brice Corgnet & Mark DeSantis & Christoph Siemroth, 2023. "Algorithmic Trading, Price Efficiency and Welfare: An Experimental Approach," Working Papers 2313, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    2. Broman, Markus S., 2020. "Local demand shocks, excess comovement and return predictability," Journal of Banking & Finance, Elsevier, vol. 119(C).
    3. Zhou, Shengjie & Ye, Qing, 2023. "Margin trading and spillover effects: Evidence from the Chinese stock markets," Emerging Markets Review, Elsevier, vol. 54(C).
    4. Ibikunle, Gbenga & Rzayev, Khaladdin, 2023. "Volatility and dark trading: Evidence from the Covid-19 pandemic," The British Accounting Review, Elsevier, vol. 55(4).
    5. Rzayev, Khaladdin & Ibikunle, Gbenga, 2019. "A state-space modeling of the information content of trading volume," Journal of Financial Markets, Elsevier, vol. 46(C).
    6. Takuo Higashide & Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2021. "New Dataset for Forecasting Realized Volatility: Is the Tokyo Stock Exchange Co-Location Dataset Helpful for Expansion of the Heterogeneous Autoregressive Model in the Japanese Stock Market?," JRFM, MDPI, vol. 14(5), pages 1-18, May.
    7. Su, Zhi & Liu, Peng & Fang, Tong, 2022. "Uncertainty matters in US financial information spillovers: Evidence from a directed acyclic graph approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 229-242.
    8. Aliyev, Nihad & Huseynov, Fariz & Rzayev, Khaladdin, 2022. "Algorithmic trading and investment-to-price sensitivity," LSE Research Online Documents on Economics 118844, London School of Economics and Political Science, LSE Library.
    9. Faten Ben Bouheni & Manish Tewari, 2023. "Common risk factors and risk–return trade-off for REITs and treasuries," Journal of Asset Management, Palgrave Macmillan, vol. 24(5), pages 374-395, September.
    10. David Easley & Marcos López de Prado & Maureen O’Hara & Zhibai Zhang & Wei Jiang, 2021. "Microstructure in the Machine Age [The risk of machine learning]," The Review of Financial Studies, Society for Financial Studies, vol. 34(7), pages 3316-3363.
    11. Khairul Zharif Zaharudin & Martin R. Young & Wei‐Huei Hsu, 2022. "High‐frequency trading: Definition, implications, and controversies," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 75-107, February.
    12. Li, Zeming & Sakkas, Athanasios & Urquhart, Andrew, 2022. "Intraday time series momentum: Global evidence and links to market characteristics," Journal of Financial Markets, Elsevier, vol. 57(C).
    13. Pankaj K. Jain & Mohamed Mekhaimer & Sandra Mortal, 2020. "Commonality in liquidity and multilateral trading facilities," The Financial Review, Eastern Finance Association, vol. 55(3), pages 481-502, August.
    14. Rzayev, Khaladdin & Ibikunle, Gbenga & Steffen, Tom, 2023. "The market quality implications of speed in cross-platform trading: evidence from Frankfurt-London microwave," LSE Research Online Documents on Economics 119989, London School of Economics and Political Science, LSE Library.
    15. 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).
    16. 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).
    17. 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).
    18. Ma, Rui & Anderson, Hamish D. & Marshall, Ben R., 2018. "Market volatility, liquidity shocks, and stock returns: Worldwide evidence," Pacific-Basin Finance Journal, Elsevier, vol. 49(C), pages 164-199.
    19. Arumugam, Devika & Prasanna, P. Krishna & Marathe, Rahul R., 2023. "Do algorithmic traders exploit volatility?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    20. Suardi, Sandy & Xu, Caihong & Zhou, Z. Ivy, 2022. "COVID-19 pandemic and liquidity commonality," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 78(C).

    More about this item

    Keywords

    COVID-19; dark pools; volatility; liquidity; informational efficiency; market quality;
    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
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G18 - Financial Economics - - General Financial Markets - - - 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:ehl:lserod:118914. 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: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .

    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.