IDEAS home Printed from https://ideas.repec.org/a/cup/jfinqa/v53y2018i02p613-641_00.html
   My bibliography  Save this article

High-Frequency Quoting: Short-Term Volatility in Bids and Offers

Author

Listed:
  • Hasbrouck, Joel

Abstract

At subsecond horizons, bids and offers in U.S. equity markets are more volatile than what would be implied by long-term fundamentals. To assess costs and consequences, this paper suggests that traders’ random delays (latencies) interact with quote volatility to generate execution price risk and relative latency costs. Analysis of the behavior of quote setters suggests that this volatility is more likely to arise from recurrent cycles of undercutting similar to the Edgeworth cycles found in product markets rather than mixed strategies of limit-order placement.

Suggested Citation

  • Hasbrouck, Joel, 2018. "High-Frequency Quoting: Short-Term Volatility in Bids and Offers," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(2), pages 613-641, April.
    • Handle: RePEc:cup:jfinqa:v:53:y:2018:i:02:p:613-641_00
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S0022109017001053/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Cenesizoglu, Tolga & Dionne, Georges & Zhou, Xiaozhou, 2022. "Asymmetric effects of the limit order book on price dynamics," Journal of Empirical Finance, Elsevier, vol. 65(C), pages 77-98.
    2. 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.
    3. Aditya Nittur Anantha & Shashi Jain, 2024. "Forecasting High Frequency Order Flow Imbalance," Papers 2408.03594, arXiv.org.
    4. Bongaerts, Dion & Achter, Mark Van, 2021. "Competition among liquidity providers with access to high-frequency trading technology," Journal of Financial Economics, Elsevier, vol. 140(1), pages 220-249.
    5. Wu, Ming & Ohk, Ki Yool, 2023. "Who benefits more? Shanghai-Hong Kong stock Connect—“Through Train”," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 409-427.
    6. Baldauf, Markus & Mollner, Joshua, 2022. "Fast traders make a quick buck: The role of speed in liquidity provision," Journal of Financial Markets, Elsevier, vol. 58(C).
    7. Li, Mingsheng & Liu, Desheng & Peng, Hongfeng & Zhang, Luxiu, 2020. "Does low synchronicity mean more or less informative prices? Evidence from an emerging market," Journal of Financial Stability, Elsevier, vol. 51(C).
    8. Matteo Aquilina & Sean Foley & Peter O'Neill & Matteo Thomas Ruf, 2023. "Sharks in the dark: quantifying HFT dark pool latency arbitrage," BIS Working Papers 1115, Bank for International Settlements.
    9. Joel Hasbrouck, 2021. "Price Discovery in High Resolution," Journal of Financial Econometrics, Oxford University Press, vol. 19(3), pages 395-430.
    10. Banerjee, Anirban & Roy, Prince, 2023. "High-frequency traders’ evolving role as market makers," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    11. Aggarwal, Nidhi & Panchapagesan, Venkatesh & Thomas, Susan, 2023. "When is the order-to-trade ratio fee effective?," Journal of Financial Markets, Elsevier, vol. 62(C).
    12. 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.
    13. Zhicheng Li & Haipeng Xing, 2022. "High-Frequency Quote Volatility Measurement Using a Change-Point Intensity Model," Mathematics, MDPI, vol. 10(4), pages 1-24, February.
    14. 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).
    15. Vladimir Petrov & Anton Golub & Richard Olsen, 2019. "Instantaneous Volatility Seasonality of High-Frequency Markets in Directional-Change Intrinsic Time," JRFM, MDPI, vol. 12(2), pages 1-31, April.
    16. Aquilina, Matteo & Foley, Sean & O'Neill, Peter & Ruf, Thomas, 2024. "Sharks in the dark: Quantifying HFT dark pool latency arbitrage," Journal of Economic Dynamics and Control, Elsevier, vol. 158(C).
    17. Rif, Alexandru & Utz, Sebastian, 2021. "Short-term stock price reversals after extreme downward price movements," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 123-133.
    18. Conrad, Jennifer & Wahal, Sunil, 2020. "The term structure of liquidity provision," Journal of Financial Economics, Elsevier, vol. 136(1), pages 239-259.
    19. Nicholas Hirschey, 2021. "Do High-Frequency Traders Anticipate Buying and Selling Pressure?," Management Science, INFORMS, vol. 67(6), pages 3321-3345, June.
    20. Ge, Hengshun & Yang, Haijun & Doukas, John A., 2024. "The optimal strategies of competitive high-frequency traders and effects on market liquidity," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 653-679.
    21. Tveito, Andreas, 2019. "Coordination and price leadership in an unregulated environment," Working Papers in Economics 4/19, University of Bergen, Department of Economics.
    22. Armstrong, Will J. & Cardella, Laura & Sabah, Nasim, 2021. "Information shocks, disagreement, and drift," Journal of Financial Economics, Elsevier, vol. 140(3), pages 916-940.
    23. Gangadhar Nayak & Amit Kumar Singh & Dilip Senapati, 2021. "Computational Modeling of Non-Gaussian Option Price Using Non-extensive Tsallis’ Entropy Framework," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1353-1371, April.
    24. Zhang, Hanyu & Dufour, Alfonso, 2019. "Modeling intraday volatility of European bond markets: A data filtering application," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 131-146.
    25. Conlon, Thomas & Cotter, John & Gençay, Ramazan, 2018. "Long-run wavelet-based correlation for financial time series," European Journal of Operational Research, Elsevier, vol. 271(2), pages 676-696.

    More about this item

    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:cup:jfinqa:v:53:y:2018:i:02:p:613-641_00. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/jfq .

    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.