English  |  正體中文  |  简体中文  |  Post-Print筆數 : 20 |  Items with full text/Total items : 90029/119959 (75%)
Visitors : 24038233      Online Users : 173
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/12409

    Title: Long Memory and Sampling Frequencies: Evidence in Stock Index Futures Markets
    Authors: 謝淑貞
    Keywords: Long memory;detrended fluctuation analysis;contrarian strategy;ARFIMA (p, d, q)
    Date: 2006-03
    Issue Date: 2008-12-03 13:48:20 (UTC+8)
    Abstract: The long-term dependent behavior in the close prices of the S&P 500, Nikkei 225, and Dow Jones index futures contracts are investigated by using the ARFIMA (p, d, q) model to estimate the order of the fractional integration parameters for a large range of sampling frequencies: from one-minute to monthly frequencies. The empirical evidence shows that the close prices exhibit anti-persistence properties for most of the sampling frequencies. This suggests that the contrarian's trading strategies in relation to stock index futures markets have a positive value. Moreover, the empirical evidence indicates that the higher frequency of the data, the stronger degree of contrarian behaviors, particularly for S&P 500 and Dow Jones stock index futures contracts.
    Relation: International Journal of Theoretical and Applied Finance, 9(5), 787-799
    Data Type: article
    Appears in Collections:[國際經營與貿易學系 ] 期刊論文

    Files in This Item:

    File Description SizeFormat
    787-799.pdf201KbAdobe PDF687View/Open

    All items in 政大典藏 are protected by copyright, with all rights reserved.

    社群 sharing

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback