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Empirical study of ARFIMA model based on fractional differencing

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  • Xiu, Jin
  • Jin, Yao

Abstract

In this paper, we studied the long-term memory of Hong Kong Hang Sheng index using MRS analysis, established ARFIMA model for it, and detailed the procedure of fractional differencing. Furthermore, we compared the ARFIMA model built by this means with the one that took first-order differencing as an alternative. The result showed that, if doing so, much useful information of time series would be lost. The forecast formula of ARFIMA model was corrected according to the method of fractional differencing, and was employed in the empirical study. It was illustrated that the forecast performance of ARFIMA model was not as good as we expected since the ARFIMA model was ineffective in forecasting Hang Sheng index. The certainty of this conclusion was proposed from two different aspects.

Suggested Citation

  • Xiu, Jin & Jin, Yao, 2007. "Empirical study of ARFIMA model based on fractional differencing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 377(1), pages 138-154.
  • Handle: RePEc:eee:phsmap:v:377:y:2007:i:1:p:138-154
    DOI: 10.1016/j.physa.2006.11.030
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    References listed on IDEAS

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    1. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
    2. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    3. Ellis, Craig & Wilson, Patrick, 2004. "Another look at the forecast performance of ARFIMA models," International Review of Financial Analysis, Elsevier, vol. 13(1), pages 63-81.
    4. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    5. Cheung, Yin-Wong & Lai, Kon S., 1995. "A search for long memory in international stock market returns," Journal of International Money and Finance, Elsevier, vol. 14(4), pages 597-615, August.
    6. Pan, Ming-Shiun & Liu, Y. Angela & Chan, Kam C., 1996. "An examination of long-term dependence in black market exchange rates in eight Pacific-Basin countries," International Review of Economics & Finance, Elsevier, vol. 5(2), pages 175-185.
    7. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    8. Hosking, Jonathan R. M., 1996. "Asymptotic distributions of the sample mean, autocovariances, and autocorrelations of long-memory time series," Journal of Econometrics, Elsevier, vol. 73(1), pages 261-284, July.
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