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Comparing the performance of FA, DFA and DMA using different synthetic long-range correlated time series

Author

Listed:
  • Ying-Hui Shao

    (ECUST)

  • Gao Feng Gu

    (ECUST)

  • Zhi-Qiang Jiang

    (ECUST)

  • Wei-Xing Zhou

    (ECUST)

  • Didier Sornette

    (ETH Zurich)

Abstract

Notwithstanding the significant efforts to develop estimators of long-range correlations (LRC) and to compare their performance, no clear consensus exists on what is the best method and under which conditions. In addition, synthetic tests suggest that the performance of LRC estimators varies when using different generators of LRC time series. Here, we compare the performances of four estimators [Fluctuation Analysis (FA), Detrended Fluctuation Analysis (DFA), Backward Detrending Moving Average (BDMA), and centred Detrending Moving Average (CDMA)]. We use three different generators [Fractional Gaussian Noises, and two ways of generating Fractional Brownian Motions]. We find that CDMA has the best performance and DFA is only slightly worse in some situations, while FA performs the worst. In addition, CDMA and DFA are less sensitive to the scaling range than FA. Hence, CDMA and DFA remain "The Methods of Choice" in determining the Hurst index of time series.

Suggested Citation

  • Ying-Hui Shao & Gao Feng Gu & Zhi-Qiang Jiang & Wei-Xing Zhou & Didier Sornette, 2012. "Comparing the performance of FA, DFA and DMA using different synthetic long-range correlated time series," Papers 1208.4158, arXiv.org.
  • Handle: RePEc:arx:papers:1208.4158
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    File URL: http://arxiv.org/pdf/1208.4158
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    Cited by:

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    2. Gulich, Damián & Zunino, Luciano, 2014. "A criterion for the determination of optimal scaling ranges in DFA and MF-DFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 397(C), pages 17-30.
    3. Kristoufek, Ladislav, 2014. "Leverage effect in energy futures," Energy Economics, Elsevier, vol. 45(C), pages 1-9.
    4. Gao-Feng Gu & Xiong Xiong & Yong-Jie Zhang & Wei Chen & Wei Zhang & Wei-Xing Zhou, 2014. "Stylized facts of price gaps in limit order books: Evidence from Chinese stocks," Papers 1405.1247, arXiv.org.
    5. Shi, Wen & Zou, Rui-biao & Wang, Fang & Su, Le, 2015. "A new image segmentation method based on multifractal detrended moving average analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 197-205.
    6. Fang, Wen & Ke, Jinchuan & Wang, Jun & Feng, Ling, 2016. "Linking market interaction intensity of 3D Ising type financial model with market volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 531-542.
    7. Alvarez-Ramirez, J. & Echeverria, J.C. & Meraz, M. & Rodriguez, E., 2017. "Asymmetric acceleration/deceleration dynamics in heart rate variability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 213-224.
    8. Hongli Niu & Jun Wang, 2014. "Phase and multifractality analyses of random price time series by finite-range interacting biased voter system," Computational Statistics, Springer, vol. 29(5), pages 1045-1063, October.
    9. Jiang, Zhi-Qiang & Xie, Wen-Jie & Zhou, Wei-Xing, 2014. "Testing the weak-form efficiency of the WTI crude oil futures market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 235-244.
    10. Xie, Wen-Jie & Jiang, Zhi-Qiang & Zhou, Wei-Xing, 2014. "Extreme value statistics and recurrence intervals of NYMEX energy futures volatility," Economic Modelling, Elsevier, vol. 36(C), pages 8-17.
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    13. Jian Zhou & Gao-Feng Gu & Zhi-Qiang Jiang & Xiong Xiong & Wei Chen & Wei Zhang & Wei-Xing Zhou, 2014. "Computational experiments successfully predict the emergence of autocorrelations in ultra-high-frequency stock returns," Papers 1404.1051, arXiv.org, revised Feb 2018.
    14. repec:eee:phsmap:v:490:y:2018:i:c:p:1295-1308 is not listed on IDEAS
    15. Zhi-Qiang Jiang & Wen-Jie Xie & Wei-Xing Zhou, 2012. "Testing the weak-form efficiency of the WTI crude oil futures market," Papers 1211.4686, arXiv.org.
    16. Sidorov, S.P. & Faizliev, A.R. & Balash, V.A. & Korobov, E.A., 2016. "Long-range correlation analysis of economic news flow intensity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 205-212.
    17. repec:eee:phsmap:v:495:y:2018:i:c:p:463-474 is not listed on IDEAS
    18. Kristoufek, Ladislav, 2014. "Detrending moving-average cross-correlation coefficient: Measuring cross-correlations between non-stationary series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 169-175.
    19. Kiyono, Ken & Tsujimoto, Yutaka, 2016. "Nonlinear filtering properties of detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 807-815.

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