Hurst Exponent Estimation Based on Moving Average Method

  • Nianpeng Wang
  • Yanheng Li
  • Hong Zhang
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 72)


In this paper, we introduce moving average method to estimate the Hurst exponent of the Hang Seng Index data for the 22-year period, from December 31, 1986, to June 6, 2008 in the Hongkong stock market, a total of 5315 trading days. Further, we present a detailed comparison between the regular rescaled range method and the moving average method. We find that the long-range correlations are present by both the new method and the regular method.


Hurst exponent Moving average method Time series analysis Rescaled range Long-range correlation 


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  1. 1.
    Hurst, H.E.: Long-term Storage capacity of Reservoirs. Transactions of the American Society of Civil Engineers 116, 770–808 (1951)Google Scholar
  2. 2.
    Carbone, A., Castelli, G., Stanley, H.: Time dependent Hurst exponent in financial time series. Physica A 344, 267–271 (2004)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Couillard, M., Davison, M.: A comment on measuring the Hurst exponent of financial time series. Physica A 348, 404–418 (2005)CrossRefGoogle Scholar
  4. 4.
    Ausloos, M., Vandewalle, N., Boveroux, P.: Applications of statistical physics to economic and Financial topics. Physica A 274, 229–240 (1999)CrossRefGoogle Scholar
  5. 5.
    Chen, C.-c., Lee, Y.-T., Chang, Y.-F.: A relationship between Hurst exponents of slip and waiting time data of earthquakes. Physica A: Statistical Mechanics and its Applications 387, 4643–4648 (2008)CrossRefGoogle Scholar
  6. 6.
    Yang, Y.-g., Yuan, J.-f., Chen, S.-z.: R/S Analysis and its Application in the Forecast of Mine Inflows. Journal of China University of Mining and Technology 16, 425–428 (2006)CrossRefGoogle Scholar
  7. 7.
    Koutsoyiannis, D.: Nonstationarity versus scaling in hydrology. Journal of Hydrology 324, 239–254 (2006)CrossRefGoogle Scholar
  8. 8.
    Hong, Z., Keqiang, D.: Multifractal Analysis of Traffic Flow Time Series. Journal of Hebei University of Engineering 2009 26, 109–112 (2009)Google Scholar
  9. 9.
    Yau, H.-Y., Nieh, C.-C.: Testing for cointegration with threshold effect between stock prices and exchange rates in Japan and Taiwan. Japan and the World Economy 21, 292–300 (2009)CrossRefGoogle Scholar
  10. 10.
    Mazouz, K., Joseph, N.L., Joulmer, J.: Stock price reaction following large one-day price changes: UK evidence. Journal of Banking & Finance 33, 1481–1493 (2009)CrossRefGoogle Scholar
  11. 11.
    Yudong, Z., Lenan, W.: Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network. Expert Systems with Applications 36, 8849–8854 (2009)CrossRefGoogle Scholar
  12. 12.
    Hsu, Y.-T., Liu, M.-C., Yeh, J., Hung, H.-F.: Forecasting the turning time of stock market based on Markov–Fourier grey model. Expert Systems with Applications 36, 8597–8603 (2009)CrossRefGoogle Scholar
  13. 13.
    Majhi, R., Panda, G., Sahoo, G.: Development and performance evaluation of FLANN based model for forecasting of stock markets. Expert Systems with Applications 36, 6800–6808 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Nianpeng Wang
    • 1
  • Yanheng Li
    • 2
  • Hong Zhang
    • 1
  1. 1.Department of MathematicsHebei University of EngineeringHandanP.R. of China
  2. 2.Key Laboratory of Resource Exploration Research of Hebei ProvinceHebei University of EngineeringHandanP.R. of China

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