• DocumentCode
    2669171
  • Title

    Applications of Ensemble Empirical mode decomposition to stock-futures basis analysis

  • Author

    Sun, Jingliang ; Sheng, Huanye

  • Author_Institution
    Dept. of Comput. Sci. & Technol., Shanghai Jiao Tong Univ., Shanghai, China
  • fYear
    2010
  • fDate
    17-19 Sept. 2010
  • Firstpage
    396
  • Lastpage
    399
  • Abstract
    In rational, efficiently functioning markets, the returns on stock index and stock index futures contracts should be perfectly, contemporaneously correlated. But in the first two month, Chinese stock index futures contracts exhibited persistent departures from fair price, offering potentially profitable arbitrage opportunities. In this paper, we examine the stock-futures basis of CSI 300 recorded every 5 min over the period from April 16, 2010 to June 10, 2010 (1872 total data points). Ensemble Empirical mode decomposition (EEMD) is a time-frequency analysis method which has been developed and widely used for non-stationary and non-linear time series analysis. In the present study, we apply the EEMD to analyze the stock-futures basis series. As a result, we extract a monotonic decreasing trends from the series which implies the market becomes more and more efficient.
  • Keywords
    stock markets; time series; time-frequency analysis; CSI 300; Chinese stock index futures contracts; ensemble empirical mode decomposition; fair price; monotonic decreasing trend; nonlinear time series analysis; nonstationary time series analysis; potentially profitable arbitrage opportunities; stock-futures basis series; time-frequency analysis method; Band pass filters; Contracts; Indexes; Time frequency analysis; Time series analysis; Transforms; White noise; ensemble empirical mode decomposition; financial time series; non-stationary; stock index futures;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information and Financial Engineering (ICIFE), 2010 2nd IEEE International Conference on
  • Conference_Location
    Chongqing
  • Print_ISBN
    978-1-4244-6927-7
  • Type

    conf

  • DOI
    10.1109/ICIFE.2010.5609386
  • Filename
    5609386