IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2011.10930.html
   My bibliography  Save this paper

Real-Time Detection of Volatility in Liquidity Provision

Author

Listed:
  • Matthew Brigida

Abstract

Previous research has found that high-frequency traders will vary the bid or offer price rapidly over periods of milliseconds. This is a benefit to fast traders who can time their trades with microsecond precision, however it is a cost to the average market participant due to increased trade execution price uncertainty. In this analysis we attempt to construct real-time methods for determining whether the liquidity of a security is being altered rapidly. We find a four-state Markov switching model identifies a state where liquidity is being rapidly varied about a mean value. This state can be used to generate a signal to delay market participant orders until the price volatility subsides. Over our sample, the signal would delay orders, in aggregate, over 0 to 10% of the trading day. Each individual delay would only last tens of milliseconds, and so would not be noticeable by the average market participant.

Suggested Citation

  • Matthew Brigida, 2020. "Real-Time Detection of Volatility in Liquidity Provision," Papers 2011.10930, arXiv.org.
    • Handle: RePEc:arx:papers:2011.10930
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2011.10930
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Joel Hasbrouck, 2003. "Intraday Price Formation in U.S. Equity Index Markets," Journal of Finance, American Finance Association, vol. 58(6), pages 2375-2400, December.
    2. Lee, Charles M C & Mucklow, Belinda & Ready, Mark J, 1993. "Spreads, Depths, and the Impact of Earnings Information: An Intraday Analysis," The Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 345-374.
    3. 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.
    4. 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.
    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. Vinay Patel, 2015. "Price Discovery in US and Australian Stock and Options Markets," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 27, July-Dece.
    2. Qin Wang & Hsiao-Fen Yang, 2015. "Earnings announcements, trading volume, and price discovery: evidence from dual class firms," Review of Quantitative Finance and Accounting, Springer, vol. 44(4), pages 669-700, May.
    3. Danny Lo, 2015. "Essays in Market Microstructure and Investor Trading," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 4-2015.
    4. Lin, Chu-Bin & Chou, Robin K. & Wang, George H.K., 2018. "Investor sentiment and price discovery: Evidence from the pricing dynamics between the futures and spot markets," Journal of Banking & Finance, Elsevier, vol. 90(C), pages 17-31.
    5. Blankespoor, Elizabeth & deHaan, Ed & Marinovic, Iván, 2020. "Disclosure processing costs, investors’ information choice, and equity market outcomes: A review," Journal of Accounting and Economics, Elsevier, vol. 70(2).
    6. Ben Ammar, Imen & Hellara, Slaheddine & Ghadhab, Imen, 2020. "High-frequency trading and stock liquidity: An intraday analysis," Research in International Business and Finance, Elsevier, vol. 53(C).
    7. Vinay Patel, 2015. "Price Discovery in US and Australian Stock and Options Markets," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 6-2015.
    8. Damien Wallace & Petko S. Kalev & Guanhua Lian, 2019. "The evolution of price discovery in us equity and derivatives markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(9), pages 1122-1136, September.
    9. Danny Lo, 2015. "Essays in Market Microstructure and Investor Trading," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 22, July-Dece.
    10. Nilabhra Bhattacharya & Bidisha Chakrabarty & Xu (Frank) Wang, 2020. "High-frequency traders and price informativeness during earnings announcements," Review of Accounting Studies, Springer, vol. 25(3), pages 1156-1199, September.
    11. Craig W. Holden & Stacey Jacobsen & Avanidhar Subrahmanyam, 2014. "The Empirical Analysis of Liquidity," Foundations and Trends(R) in Finance, now publishers, vol. 8(4), pages 263-365, December.
    12. 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.
    13. Jos Van Bommel & Jay Dahya & Zhihong Shi, 2010. "An empirical investigation of the speed of information aggregation: a study of IPOs," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 2(1), pages 47-79.
    14. Joshua Madsen & Marina Niessner, 2019. "Is Investor Attention for Sale? The Role of Advertising in Financial Markets," Journal of Accounting Research, Wiley Blackwell, vol. 57(3), pages 763-795, June.
    15. Gur Huberman & Dominika Halka, 2001. "Systematic Liquidity," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 24(2), pages 161-178, June.
    16. Bruno Biais & Fany Declerck & Sophie Moinas, 2016. "Who supplies liquidity, how and when?," BIS Working Papers 563, Bank for International Settlements.
    17. Yanyi Ye & Yun Wang & Xiaoguang Yang, 2022. "Bank loan information and information asymmetry in the stock market: evidence from China," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-28, December.
    18. Baruch, Shmuel & Panayides, Marios & Venkataraman, Kumar, 2017. "Informed trading and price discovery before corporate events," Journal of Financial Economics, Elsevier, vol. 125(3), pages 561-588.
    19. Christiane Goodfellow & Martin T. Bohl, 2011. "Forestalling Floor Closure: Evidence from a Natural Experiment on the German Stock Market," Post-Print hal-00676103, HAL.
    20. Lepone, Andrew & Yang, Jin Young, 2013. "Informational role of market makers: The case of exchange traded CFDs," Journal of Empirical Finance, Elsevier, vol. 23(C), pages 84-92.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2011.10930. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.