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Size clustering in the FTSE-100 index futures market

Author

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  • O. Gwilym
  • L. Meng

    (UMR CNRS 8179 - Université de Lille, Sciences et Technologies - CNRS - Centre National de la Recherche Scientifique)

Abstract

Recent literature has begun to explore size clustering in financial markets. If a market is perfectly liquid, traders should be able to trade the exact amount that they desire; however, the presence of size clustering may prevent them from achieving optimal trade sizes. This study is novel in its investigation of size clustering in a futures market. We find that trade sizes cluster in a manner that is similar to the pattern of price clustering found in many financial markets. Importantly, we identify a trade‐off between size resolution and price resolution. We also find that the number of distinct trade sizes increases with trade frequency and with intra‐day volatility, and increases at the end of each calendar quarter. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:432–443, 2010
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • O. Gwilym & L. Meng, 2010. "Size clustering in the FTSE-100 index futures market," Post-Print hal-00569087, HAL.
    • Handle: RePEc:hal:journl:hal-00569087
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    Cited by:

    1. Qin Wang & Jun Zhang, 2016. "Trade Size Clustering In The E-Mini Index Futures Markets," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 39(3), pages 247-262, September.
    2. Chen, Tao, 2019. "Trade-size clustering and price efficiency," Japan and the World Economy, Elsevier, vol. 49(C), pages 195-203.
    3. Mishra, Ajay Kumar & Tripathy, Trilochan, 2018. "Price and trade size clustering: Evidence from the national stock exchange of India," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 63-72.
    4. Fernando Palao & Ángel Pardo Tornero, 2012. "When size matters: Clustering in the European Carbon Market," Working Papers. Serie EC 2012-10, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    5. Lin William Cong & Xi Li & Ke Tang & Yang Yang, 2021. "Crypto Wash Trading," Papers 2108.10984, arXiv.org.
    6. Garvey, Ryan & Wu, Fei, 2014. "Clustering of intraday order-sizes by uninformed versus informed traders," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 222-235.
    7. Palao, Fernando & Pardo, Ángel, 2014. "What makes carbon traders cluster their orders?," Energy Economics, Elsevier, vol. 43(C), pages 158-165.

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