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Heavy-tailed features and dependence in limit order book volume profiles in futures markets

Author

Listed:
  • Kylie-Anne Richards

    (School of Mathematics and Statistics, University of NSW, UNSW Sydney, NSW 2052, Australia;
    Boronia Capital Pty. Ltd., 12 Holtermann Street, Crows Nest, NSW 2065, Australia)

  • Gareth W. Peters

    (School of Mathematics and Statistics, University of NSW, UNSW Sydney, NSW 2052, Australia;
    Boronia Capital Pty. Ltd., 12 Holtermann Street, Crows Nest, NSW 2065, Australia;
    Department of Statistical Science, University College London, UK;
    Oxford Mann Institute, Oxford University, Oxford, UK;)

  • William Dunsmuir

    (School of Mathematics and Statistics, University of NSW, UNSW Sydney, NSW 2052, Australia)

Abstract

This paper investigates fundamental stochastic attributes of the random structures of the volume profiles of the limit order book. We find statistical evidence that heavy-tailed sub-exponential volume profiles occur on the limit order book and these features are best captured via the generalized Pareto distribution MLE method. In futures exchanges, the heavy tail features are not asset class dependent and occur on ultra or mid-range high frequency. Volume forecasting models should account for heavy tails, time varying parameters and long memory. In application, utilizing the generalized Pareto distribution to model volume profiles allows one to avoid over-estimating the round trip cost of trading.

Suggested Citation

  • Kylie-Anne Richards & Gareth W. Peters & William Dunsmuir, 2015. "Heavy-tailed features and dependence in limit order book volume profiles in futures markets," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 2(03), pages 1-56.
    • Handle: RePEc:wsi:ijfexx:v:02:y:2015:i:03:n:s2424786315500334
    DOI: 10.1142/S2424786315500334
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    References listed on IDEAS

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