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Trading Large VWAP Orders in Discrete Time

  • Author / Creator
    Zeng, Linghang
  • This thesis focuses on characterizing an optimal trading strategy for a large trader, who has to buy (or sell) a fixed large volume over a given time period. We propose a model in discrete time, based on VWAP (Volume Weighted Average Price). The objective is to minimize expected deviations between the trader's relative volumes and the market relative volumes at all times. By applying dynamic programming, we characterize the optimal strategy under three different assumptions on the intraday market volumes: i.i.d.~volumes, general independent volumes and independent Gamma distributed volumes. The optimal strategy under the last assumption is meaningful and explicit. For three exemplary Chinese stocks, we present its good data fit and illustrate the improved performance (reduced deviations to the market relative volumes) compared with the empirical strategy, which is one of the most popular and efficient VWAP strategies in the financial industry.

  • Subjects / Keywords
  • Graduation date
    2013-11
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R30C4SS4X
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Mathematical and Statistical Sciences
  • Specialization
    • Mathematical Finance
  • Supervisor / co-supervisor and their department(s)
    • Frei, Christoph (Mathematical and Statistical Sciences)
  • Examining committee members and their departments
    • Melnikov, Alexander (Mathematical and Statistical Sciences)
    • Choulli, Tahir (Mathematical and Statistical Sciences)
    • Kouritzin, Mike (Mathematical and Statistical Sciences)