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Multifractal detrended fluctuation analysis on high-frequency SZSE in Chinese stock market

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  • Gu, Danlei
  • Huang, Jingjing

Abstract

We use multifractal detrended fluctuation analysis (MF-DFA) method to investigate the multifractal behavior of Shenzhen Component Index (SZSE) 5-minute high-frequency stock data from 2017.6.15 - 2018.4.11. We determine generalized Hurst exponent and singularity spectrum and find that these fluctuations have multifractal nature. In order to maintain the long-term memory of the stock , all 9696 data are divided into 6 units. According to the multifractal spectrum, the main parameters of the six units are obtained. Comparing the MF-DFA results for the original SZSE high-frequency time series with those for shuffled series, we conclude that the origin of multifractality is due to both the broadness of probability density function and long-range correlation. The generalized Hurst exponent obviously depend on the order of fluctuation function and change with it. The curve of scaling function clearly departs from a straight line, i.e. it shows clearly nonlinear property, and the multifractal spectrum displays the commonly observed bell shape. This will provide an important and theoretical foundation for researching the forecasting of finance markets.

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  • Gu, Danlei & Huang, Jingjing, 2019. "Multifractal detrended fluctuation analysis on high-frequency SZSE in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 225-235.
    • Handle: RePEc:eee:phsmap:v:521:y:2019:i:c:p:225-235
    DOI: 10.1016/j.physa.2019.01.040
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    References listed on IDEAS

    as
    1. Chen, Cheng & Wang, Yudong, 2017. "Understanding the multifractality in portfolio excess returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 346-355.
    2. Maganini, Natália Diniz & Da Silva Filho, Antônio Carlos & Lima, Fabiano Guasti, 2018. "Investigation of multifractality in the Brazilian stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 497(C), pages 258-271.
    3. Wei, Yu & Wang, Peng, 2008. "Forecasting volatility of SSEC in Chinese stock market using multifractal analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(7), pages 1585-1592.
    4. Dutta, Srimonti & Ghosh, Dipak & Chatterjee, Sucharita, 2016. "Multifractal detrended Cross Correlation Analysis of Foreign Exchange and SENSEX fluctuation in Indian perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 188-201.
    5. Junhuan Zhang & Jun Wang & Jiguang Shao, 2010. "Finite-Range Contact Process On The Market Return Intervals Distributions," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 13(05), pages 643-657.
    6. Parameswaran Gopikrishnan & Vasiliki Plerou & Luis A. Nunes Amaral & Martin Meyer & H. Eugene Stanley, 1999. "Scaling of the distribution of fluctuations of financial market indices," Papers cond-mat/9905305, arXiv.org.
    7. Grech, Dariusz, 2016. "Alternative measure of multifractal content and its application in finance," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 183-195.
    8. Norouzzadeh, P. & Jafari, G.R., 2005. "Application of multifractal measures to Tehran price index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 356(2), pages 609-627.
    9. Shi, Wenbin & Shang, Pengjian & Wang, Jing & Lin, Aijing, 2014. "Multiscale multifractal detrended cross-correlation analysis of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 35-44.
    10. Liu, Zhicao & Ye, Yong & Ma, Feng & Liu, Jing, 2017. "Can economic policy uncertainty help to forecast the volatility: A multifractal perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 181-188.
    11. Zhao, Xiaojun & Shang, Pengjian & Shi, Wenbin, 2014. "Multifractal cross-correlation spectra analysis on Chinese stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 84-92.
    12. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    13. Zhang, Guofu & Li, Jingjing, 2018. "Multifractal analysis of Shanghai and Hong Kong stock markets before and after the connect program," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 611-622.
    14. Oh, Gabjin & Kim, Ho-yong & Ahn, Seok-Won & Kwak, Wooseop, 2015. "Analyzing the financial crisis using the entropy density function," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 464-469.
    15. Yang, Liansheng & Zhu, Yingming & Wang, Yudong, 2016. "Multifractal characterization of energy stocks in China: A multifractal detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 357-365.
    16. Ikeda, Taro, 2018. "Multifractal structures for the Russian stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 2123-2128.
    17. Thomas Lux & Michele Marchesi, 2000. "Volatility Clustering In Financial Markets: A Microsimulation Of Interacting Agents," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(04), pages 675-702.
    18. Wei, Yu & Huang, Dengshi, 2005. "Multifractal analysis of SSEC in Chinese stock market: A different empirical result from Heng Seng index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(2), pages 497-508.
    19. Lin, Aijing & Ma, Hui & Shang, Pengjian, 2015. "The scaling properties of stock markets based on modified multiscale multifractal detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 525-537.
    20. Benbachir, Saâd & El Alaoui, Marwane, 2011. "A Multifractal Detrended Fluctuation Analysis of the Moroccan Stock Exchange," MPRA Paper 49003, University Library of Munich, Germany.
    21. Hasan, Rashid & Mohammad, Salim M., 2015. "Multifractal analysis of Asian markets during 2007–2008 financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 746-761.
    22. Tiwari, Aviral Kumar & Albulescu, Claudiu Tiberiu & Yoon, Seong-Min, 2017. "A multifractal detrended fluctuation analysis of financial market efficiency: Comparison using Dow Jones sector ETF indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 182-192.
    23. Huang, Jingjing & Shang, Pengjian & Zhao, Xiaojun, 2012. "Multifractal diffusion entropy analysis on stock volatility in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5739-5745.
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