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. Andriy Shkilko & Konstantin Sokolov, 2020. "Every Cloud Has a Silver Lining: Fast Trading, Microwave Connectivity, and Trading Costs," Journal of Finance, American Finance Association, vol. 75(6), pages 2899-2927, December.
    2. Glosten, Lawrence R. & Milgrom, Paul R., 1985. "Bid, ask and transaction prices in a specialist market with heterogeneously informed traders," Journal of Financial Economics, Elsevier, vol. 14(1), pages 71-100, March.
    3. Terrence Hendershott & Haim Mendelson, 2000. "Crossing Networks and Dealer Markets: Competition and Performance," Journal of Finance, American Finance Association, vol. 55(5), pages 2071-2115, October.
    4. Terrence Hendershott & Charles M. Jones, 2005. "Island Goes Dark: Transparency, Fragmentation, and Regulation," The Review of Financial Studies, Society for Financial Studies, vol. 18(3), pages 743-793.
    5. Haoxiang Zhu, 2014. "Do Dark Pools Harm Price Discovery?," The Review of Financial Studies, Society for Financial Studies, vol. 27(3), pages 747-789.
    6. Comerton-Forde, Carole & Putniņš, Tālis J., 2015. "Dark trading and price discovery," Journal of Financial Economics, Elsevier, vol. 118(1), pages 70-92.
    7. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    8. Hans Degryse & Frank de Jong & Vincent van Kervel, 2015. "The Impact of Dark Trading and Visible Fragmentation on Market Quality," Review of Finance, European Finance Association, vol. 19(4), pages 1587-1622.
    9. Lin, Ji-Chai & Sanger, Gary C & Booth, G Geoffrey, 1995. "Trade Size and Components of the Bid-Ask Spread," The Review of Financial Studies, Society for Financial Studies, vol. 8(4), pages 1153-1183.
    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. Nimalendran, Mahendrarajah & Ray, Sugata, 2014. "Informational linkages between dark and lit trading venues," Journal of Financial Markets, Elsevier, vol. 17(C), pages 230-261.
    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. Ibikunle, Gbenga & Rzayev, Khaladdin, 2023. "Volatility and dark trading: Evidence from the Covid-19 pandemic," The British Accounting Review, Elsevier, vol. 55(4).
    2. Peter Gomber & Satchit Sagade & Erik Theissen & Moritz Christian Weber & Christian Westheide, 2017. "Competition Between Equity Markets: A Review Of The Consolidation Versus Fragmentation Debate," Journal of Economic Surveys, Wiley Blackwell, vol. 31(3), pages 792-814, July.
    3. Linlin Ye, 2016. "Understanding the Impacts of Dark Pools on Price Discovery," Papers 1612.08486, arXiv.org.
    4. Hatheway, Frank & Kwan, Amy & Zheng, Hui, 2017. "An Empirical Analysis of Market Segmentation on U.S. Equity Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(6), pages 2399-2427, December.
    5. Suchismita Mishra & Le Zhao, 2021. "Order Routing Decisions for a Fragmented Market: A Review," JRFM, MDPI, vol. 14(11), pages 1-32, November.
    6. Bayona, Anna & Dumitrescu, Ariadna & Manzano, Carolina, 2023. "Information and optimal trading strategies with dark pools," Economic Modelling, Elsevier, vol. 126(C).
    7. Ye, Linlin, 2024. "Understanding the impacts of dark pools on price discovery," Journal of Financial Markets, Elsevier, vol. 68(C).
    8. Gomber, Peter & Sagade, Satchit & Theissen, Erik & Weber, Moritz Christian & Westheide, Christian, 2016. "Spoilt for choice: Order routing decisions in fragmented equity markets," CFR Working Papers 16-04, University of Cologne, Centre for Financial Research (CFR).
    9. Ibikunle, Gbenga & Aquilina, Matteo & Diaz-Rainey, Ivan & Sun, Yuxin, 2021. "City goes dark: Dark trading and adverse selection in aggregate markets," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 1-22.
    10. Comerton-Forde, Carole & Putniņš, Tālis J., 2015. "Dark trading and price discovery," Journal of Financial Economics, Elsevier, vol. 118(1), pages 70-92.
    11. Ibikunle, Gbenga & Li, Youwei & Mare, Davide & Sun, Yuxin, 2021. "Dark matters: The effects of dark trading restrictions on liquidity and informational efficiency," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    12. Haoxiang Zhu, 2014. "Do Dark Pools Harm Price Discovery?," The Review of Financial Studies, Society for Financial Studies, vol. 27(3), pages 747-789.
    13. Jonathan Brogaard & Jing Pan, 2022. "Dark Pool Trading and Information Acquisition," The Review of Financial Studies, Society for Financial Studies, vol. 35(5), pages 2625-2666.
    14. Menkveld, Albert J. & Yueshen, Bart Zhou & Zhu, Haoxiang, 2017. "Shades of darkness: A pecking order of trading venues," Journal of Financial Economics, Elsevier, vol. 124(3), pages 503-534.
    15. Halim, Edward & Riyanto, Yohanes E. & Roy, Nilanjan & Wang, Yan, 2022. "The Bright Side of Dark Markets: Experiments," MPRA Paper 111803, University Library of Munich, Germany.
    16. Zhang, Zeyu & Ibikunle, Gbenga, 2023. "The market quality effects of sub-second frequent batch auctions: Evidence from dark trading restrictions," International Review of Financial Analysis, Elsevier, vol. 89(C).
    17. Michael Brolley, 2020. "Price Improvement and Execution Risk in Lit and Dark Markets," Management Science, INFORMS, vol. 66(2), pages 863-886, February.
    18. Gbenga Ibikunle & Davide Mare & Yuxin Sun, 2020. "The paradoxical effects of market fragmentation on adverse selection risk and market efficiency," The European Journal of Finance, Taylor & Francis Journals, vol. 26(14), pages 1439-1461, September.
    19. Gomber, Peter & Sagade, Satchit & Theissen, Erik & Weber, Moritz Christian & Westheide, Christian, 2023. "Spoilt for choice: Determinants of market shares in fragmented equity markets," Journal of Financial Markets, Elsevier, vol. 64(C).
    20. Foley, Sean & Putniņš, Tālis J., 2016. "Should we be afraid of the dark? Dark trading and market quality," Journal of Financial Economics, Elsevier, vol. 122(3), pages 456-481.

    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.