IDEAS home Printed from https://ideas.repec.org/p/ags/aaea20/304596.html
   My bibliography  Save this paper

Maximum Order Size and Agricultural Futures Market Quality: Evidence from a Natural Experiment

Author

Listed:
  • Peng, Kun
  • Hu, Zhepeng
  • Robe, Michel A.

Abstract

No abstract is available for this item.

Suggested Citation

  • Peng, Kun & Hu, Zhepeng & Robe, Michel A., 2020. "Maximum Order Size and Agricultural Futures Market Quality: Evidence from a Natural Experiment," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304596, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea20:304596
    DOI: 10.22004/ag.econ.304596
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/304596/files/19256.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.304596?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. 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.
    2. Conrad, Jennifer & Wahal, Sunil & Xiang, Jin, 2015. "High-frequency quoting, trading, and the efficiency of prices," Journal of Financial Economics, Elsevier, vol. 116(2), pages 271-291.
    3. Zhepeng Hu & Teresa Serra & Philip Garcia, 2020. "Algorithmic quoting, trading, and market quality in agricultural commodity futures markets," Applied Economics, Taylor & Francis Journals, vol. 52(58), pages 6277-6291, December.
    4. Anabelle Couleau & Teresa Serra & Philip Garcia, 2019. "Microstructure Noise and Realized Variance in the Live Cattle Futures Market," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 101(2), pages 563-578.
    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. Indriawan, Ivan & Martinez, Valeria & Tse, Yiuman, 2021. "The impact of the change in USDA announcement release procedures on agricultural commodity futures," Journal of Commodity Markets, Elsevier, vol. 23(C).
    2. Uctum, Remzi & Renou-Maissant, Patricia & Prat, Georges & Lecarpentier-Moyal, Sylvie, 2017. "Persistence of announcement effects on the intraday volatility of stock returns: Evidence from individual data," Review of Financial Economics, Elsevier, vol. 35(C), pages 43-56.
    3. 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.
    4. 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.
    5. Corsetti, Giancarlo & Lafarguette, Romain & Mehl, Arnaud, 2019. "Fast trading and the virtue of entropy: evidence from the foreign exchange market," Working Paper Series 2300, European Central Bank.
    6. Hagströmer, Björn, 2021. "Bias in the effective bid-ask spread," Journal of Financial Economics, Elsevier, vol. 142(1), pages 314-337.
    7. Stenfors, Alexis & Susai, Masayuki, 2021. "Spoofing and pinging in foreign exchange markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 70(C).
    8. 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.
    9. Tomy Lee, 2019. "Latency in Fragmented Markets," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 33, pages 128-153, July.
    10. Hatch, Brian C. & Johnson, Shane A. & Wang, Qin Emma & Zhang, Jun, 2021. "Algorithmic trading and firm value," Journal of Banking & Finance, Elsevier, vol. 125(C).
    11. Hautsch, Nikolaus & Noé, Michael & Zhang, S. Sarah, 2017. "The ambivalent role of high-frequency trading in turbulent market periods," CFS Working Paper Series 580, Center for Financial Studies (CFS).
    12. Breedon, Francis & Chen, Louisa & Ranaldo, Angelo & Vause, Nicholas, 2023. "Judgment day: Algorithmic trading around the Swiss franc cap removal," Journal of International Economics, Elsevier, vol. 140(C).
    13. Yang, Haijun & Ge, Hengshun & Luo, Ying, 2020. "The optimal bid-ask price strategies of high-frequency trading and the effect on market liquidity," Research in International Business and Finance, Elsevier, vol. 53(C).
    14. Cox, Justin S., 2022. "The impact of reporting changes on hidden liquidity: Evidence from the Chicago stock exchange," Global Finance Journal, Elsevier, vol. 53(C).
    15. Fabrice Rousseau & Herve Boco & Laurent Germain, 2020. "High Frequency Trading: Strategic Competition Between Slow and Fast Traders," Economics Department Working Paper Series n296-20.pdf, Department of Economics, National University of Ireland - Maynooth.
    16. Chakrabarty, Bidisha & Moulton, Pamela C. & Pascual, Roberto, 2017. "Trading system upgrades and short-sale bans: Uncoupling the effects of technology and regulation," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 74-90.
    17. Ligot, Stephanie & Gillet, Roland & Veryzhenko, Iryna, 2021. "Intraday volatility smile: Effects of fragmentation and high frequency trading on price efficiency," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    18. Zheng, Jiayi & Zhu, Yushu, 2023. "Algorithmic trading and block ownership initiation: An information perspective," The British Accounting Review, Elsevier, vol. 55(4).
    19. Stenfors, Alexis & Susai, Masayuki, 2019. "Liquidity withdrawal in the FX spot market: A cross-country study using high-frequency data," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 59(C), pages 36-57.
    20. Nicholas Hirschey, 2021. "Do High-Frequency Traders Anticipate Buying and Selling Pressure?," Management Science, INFORMS, vol. 67(6), pages 3321-3345, June.

    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:ags:aaea20:304596. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.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.