IDEAS home Printed from https://ideas.repec.org/a/wly/jfutmk/v42y2022i8p1434-1465.html
   My bibliography  Save this article

The impact of high speed quoting on execution risk dynamics: Evidence from interest rate futures markets

Author

Listed:
  • Jing Nie
  • Juliana Malagon
  • Julian Williams

Abstract

This paper intends to characterize the effect of high‐frequency quoting (HFQ) on the execution risk of Eurodollar futures. We construct a unique data set to capture the quoting and trading activities within the limit order book, which allows us to classify the realised fraction of HFQ activity within the market. We then estimate the marginal effect of the HFQ fraction on the execution risk through a novel semi‐parametric regression. The results suggest that the effect of HFQ on market quality is nonlinear with critical saturation levels. The HFQ effects on market quality seem to disappear once certain critical points are reached.

Suggested Citation

  • Jing Nie & Juliana Malagon & Julian Williams, 2022. "The impact of high speed quoting on execution risk dynamics: Evidence from interest rate futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(8), pages 1434-1465, August.
    • Handle: RePEc:wly:jfutmk:v:42:y:2022:i:8:p:1434-1465
    DOI: 10.1002/fut.22339
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/fut.22339
    Download Restriction: no
    File URL: https://libkey.io/10.1002/fut.22339?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. S. Darolles & Y. Fan & J. P. Florens & E. Renault, 2011. "Nonparametric Instrumental Regression," Econometrica, Econometric Society, vol. 79(5), pages 1541-1565, September.
    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. Albert J. Menkveld & Marius A. Zoican, 2017. "Need for Speed? Exchange Latency and Liquidity," Review of Financial Studies, Society for Financial Studies, vol. 30(4), pages 1188-1228.
    4. Admati, Anat R & Pfleiderer, Paul, 1989. "Divide and Conquer: A Theory of Intraday and Day-of-the-Week Mean Effects," Review of Financial Studies, Society for Financial Studies, vol. 2(2), pages 189-223.
    5. Albert S. Kyle & Anna A. Obizhaeva, 2016. "Market Microstructure Invariance: Empirical Hypotheses," Econometrica, Econometric Society, vol. 84, pages 1345-1404, July.
    6. Hendershott, Terrence & Moulton, Pamela C., 2011. "Automation, speed, and stock market quality: The NYSE's Hybrid," Journal of Financial Markets, Elsevier, vol. 14(4), pages 568-604, November.
    7. Lee, Charles M C & Ready, Mark J, 1991. "Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-746, June.
    8. Jean‐Pierre Florens & Jan Johannes & Sébastien Van Bellegem, 2012. "Instrumental regression in partially linear models," Econometrics Journal, Royal Economic Society, vol. 15(2), pages 304-324, June.
    9. Brogaard, Jonathan & Hendershott, Terrence & Riordan, Ryan, 2017. "High frequency trading and the 2008 short-sale ban," Journal of Financial Economics, Elsevier, vol. 124(1), pages 22-42.
    10. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    11. Harris, Lawrence, 1989. " S&P 500 Cash Stock Price Volatilities," Journal of Finance, American Finance Association, vol. 44(5), pages 1155-1175, December.
    12. Admati, Anat R, 1985. "A Noisy Rational Expectations Equilibrium for Multi-asset Securities Markets," Econometrica, Econometric Society, vol. 53(3), pages 629-657, May.
    13. Brogaard, Jonathan & Carrion, Allen & Moyaert, Thibaut & Riordan, Ryan & Shkilko, Andriy & Sokolov, Konstantin, 2018. "High frequency trading and extreme price movements," Journal of Financial Economics, Elsevier, vol. 128(2), pages 253-265.
    14. Huang, Roger D & Stoll, Hans R, 1994. "Market Microstructure and Stock Return Predictions," Review of Financial Studies, Society for Financial Studies, vol. 7(1), pages 179-213.
    15. Florens, Jean-Pierre & Johannes, Jan & Van Bellegem, Sebastien, 2012. "Instrumental regression in partially linear models," LIDAM Reprints ISBA 2012017, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    16. Eric Budish & Peter Cramton & John Shim, 2015. "Editor's Choice The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(4), pages 1547-1621.
    17. Anat R. Admati, Paul Pfleiderer, 1988. "A Theory of Intraday Patterns: Volume and Price Variability," Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 3-40.
    18. FLORENS, Jean-Pierre & JOHANNES, Jan & VAN BELLEGEM, Sébastien, 2012. "Instrumental regression in partially linear models," LIDAM Reprints CORE 2456, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    19. Albert S. Kyle & Anna A. Obizhaeva, 2016. "Market Microstructure Invariance: Empirical Hypotheses," Econometrica, Econometric Society, vol. 84(4), pages 1345-1404, July.
    20. 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.
    21. Chan, Kalok, 1992. "A Further Analysis of the Lead-Lag Relationship between the Cash Market and Stock Index Futures Market," Review of Financial Studies, Society for Financial Studies, vol. 5(1), pages 123-152.
    22. Stoll, Hans R. & Whaley, Robert E., 1990. "The Dynamics of Stock Index and Stock Index Futures Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 25(4), pages 441-468, December.
    23. Whitney K. Newey & James L. Powell, 2003. "Instrumental Variable Estimation of Nonparametric Models," Econometrica, Econometric Society, vol. 71(5), pages 1565-1578, September.
    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. Lauter, Tobias & Prokopczuk, Marcel, 2022. "Measuring commodity market quality," Journal of Banking & Finance, Elsevier, vol. 145(C).
    2. 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.
    3. Goodhart, Charles A. E. & O'Hara, Maureen, 1997. "High frequency data in financial markets: Issues and applications," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 73-114, June.
    4. Lof, Matthijs & van Bommel, Jos, 2023. "Asymmetric information and the distribution of trading volume," Journal of Corporate Finance, Elsevier, vol. 82(C).
    5. Linnenluecke, Martina K. & Chen, Xiaoyan & Ling, Xin & Smith, Tom & Zhu, Yushu, 2017. "Research in finance: A review of influential publications and a research agenda," Pacific-Basin Finance Journal, Elsevier, vol. 43(C), pages 188-199.
    6. 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.
    7. Melanie Birke & Sebastien Van Bellegem & Ingrid Van Keilegom, 2017. "Semi-parametric Estimation in a Single-index Model with Endogenous Variables," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(1), pages 168-191, March.
    8. Irene Botosaru & Chris Muris & Senay Sokullu, 2022. "Time-Varying Linear Transformation Models with Fixed Effects and Endogeneity for Short Panels," Department of Economics Working Papers 2022-01, McMaster University.
    9. Robert Litzenberger & Jeff Castura & Richard Gorelick, 2012. "The Impacts of Automation and High Frequency Trading on Market Quality," Annual Review of Financial Economics, Annual Reviews, vol. 4(1), pages 59-98, October.
    10. Mark Marner-Hausen, 2022. "Developing a Framework for Real-Time Trading in a Laboratory Financial Market," ECONtribute Discussion Papers Series 172, University of Bonn and University of Cologne, Germany.
    11. Gerig, Austin & Michayluk, David, 2017. "Automated liquidity provision," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 1-13.
    12. Marlene Haas & Marius Andrei Zoican, 2016. "Beyond the Frequency Wall: Speed and Liquidity on Batch Auction Markets," Post-Print hal-01484805, HAL.
    13. Vayanos, Dimitri & Wang, Jiang, 2013. "Market Liquidity—Theory and Empirical Evidence ," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1289-1361, Elsevier.
    14. Andy Snell & Ian Tonks, 1996. "Utilising Time Series Methods to Assess Information and Inventory Effects in a Dealer Market in Illiquid Stocks," FMG Discussion Papers dp242, Financial Markets Group.
    15. Chordia, Tarun & Roll, Richard & Subrahmanyam, Avanidhar, 2008. "Liquidity and market efficiency," Journal of Financial Economics, Elsevier, vol. 87(2), pages 249-268, February.
    16. 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).
    17. Aït-Sahalia, Yacine & Brunetti, Celso, 2020. "High frequency traders and the price process," Journal of Econometrics, Elsevier, vol. 217(1), pages 20-45.
    18. 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.
    19. 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.
    20. Snell, Andy & Tonks, Ian, 1996. "Using time series methods to assess information and inventory effects in a dealer market in Il-liquid stocks," LSE Research Online Documents on Economics 119167, London School of Economics and Political Science, LSE Library.

    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:wly:jfutmk:v:42:y:2022:i:8:p:1434-1465. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/0270-7314/ .

    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.