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Long Memory And Sampling Frequencies: Evidence In Stock Index Futures Markets

Author

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  • SHWU-JANE SHIEH

    (Department of International Trade, College of Commerce, National Cheng-Chi University, Taipei, Taiwan, R.O.C.)

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.

Suggested Citation

  • Shwu-Jane Shieh, 2006. "Long Memory And Sampling Frequencies: Evidence In Stock Index Futures Markets," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 9(05), pages 787-799.
    • Handle: RePEc:wsi:ijtafx:v:09:y:2006:i:05:n:s0219024906003780
    DOI: 10.1142/S0219024906003780
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    Citations

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    Cited by:

    1. Onder Buberkoku, 2018. "Examining the Value-at-risk Performance of Fractionally Integrated GARCH Models: Evidence from Energy Commodities," International Journal of Economics and Financial Issues, Econjournals, vol. 8(3), pages 36-50.
    2. Laura Garcia‐Jorcano & Alfonso Novales, 2021. "Volatility specifications versus probability distributions in VaR forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 189-212, March.
    3. Bagher Adabi & Mohsen Mehrara & Shapour Mohammadi, 2015. "Evaluation Approaches of Value at Risk for Tehran Stock Exchange," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 19(1), pages 41-62, Winter.
    4. Yalama, Abdullah & Celik, Sibel, 2013. "Real or spurious long memory characteristics of volatility: Empirical evidence from an emerging market," Economic Modelling, Elsevier, vol. 30(C), pages 67-72.
    5. David, S.A. & Inácio, C.M.C. & Quintino, D.D. & Machado, J.A.T., 2020. "Measuring the Brazilian ethanol and gasoline market efficiency using DFA-Hurst and fractal dimension," Energy Economics, Elsevier, vol. 85(C).
    6. Mert URAL, 2016. "Modelling Crude Oil Price Volatility and the Effects of Global Financial Crisis," Sosyoekonomi Journal, Sosyoekonomi Society, issue 24(29).
    7. Naeem, Muhammad & Shahbaz, Muhammad & Saleem, Kashif & Mustafa, Faisal, 2019. "Risk analysis of high frequency precious metals returns by using long memory model," Resources Policy, Elsevier, vol. 61(C), pages 399-409.

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