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Analysis of efficiency for Shenzhen stock market based on multifractal detrended fluctuation analysis

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  • Wang, Yudong
  • Liu, Li
  • Gu, Rongbao
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    Abstract

    We divided the whole series of Shenzhen stock market into two sub-series at the criterion of the date of a reform and their scale behaviors are investigated using multifractal detrended fluctuation analysis (MF-DFA). Employing the method of rolling window, we find that Shenzhen stock market was becoming more and more efficient by analyzing the change of Hurst exponent and a new efficient measure, which is equal to multifractality degree sometimes. We also study the change of Hurst exponent and multifractality degree of volatility series. The results show that the volatility series still have significantly long-range dependence and multifractality indicating that some conventional models such as GARCH and EGARCH cannot be used to forecast the volatilities of Shenzhen stock market. At last, the abnormal phenomenon of multifractality degrees for return series is discussed. The results have very important implications for analyzing the influence of policies, especially under the environment of financial crisis.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Review of Financial Analysis.

    Volume (Year): 18 (2009)
    Issue (Month): 5 (December)
    Pages: 271-276

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    Handle: RePEc:eee:finana:v:18:y:2009:i:5:p:271-276

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    Web page: http://www.elsevier.com/locate/inca/620166

    Related research

    Keywords: Shenzhen stock market Efficiency Scale behavior Hurst exponent Multifractality degree;

    References

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    Cited by:
    1. Yin, Yi & Shang, Pengjian, 2013. "Modified DFA and DCCA approach for quantifying the multiscale correlation structure of financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6442-6457.
    2. Zhou, Weijie & Dang, Yaoguo & Gu, Rongbao, 2013. "Efficiency and multifractality analysis of CSI 300 based on multifractal detrending moving average algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1429-1438.
    3. Ma, Feng & Wei, Yu & Huang, Dengshi, 2013. "Multifractal detrended cross-correlation analysis between the Chinese stock market and surrounding stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(7), pages 1659-1670.
    4. Zhuang, Xiaoyang & Wei, Yu & Zhang, Bangzheng, 2014. "Multifractal detrended cross-correlation analysis of carbon and crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 113-125.
    5. Wang, Yudong & Wei, Yu & Wu, Chongfeng, 2010. "Auto-correlated behavior of WTI crude oil volatilities: A multiscale perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5759-5768.
    6. Lin, Xiaoqiang & Fei, Fangyu & Wang, Yudong, 2011. "Analysis of the efficiency of the Shanghai stock market: A volatility perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3486-3495.
    7. Chong, Terence Tai-Leung & Lam, Tau-Hing & Yan, Isabel Kit-Ming, 2012. "Is the Chinese stock market really inefficient?," China Economic Review, Elsevier, vol. 23(1), pages 122-137.
    8. Wang, Yudong & Wu, Chongfeng & Pan, Zhiyuan, 2011. "Multifractal detrending moving average analysis on the US Dollar exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3512-3523.
    9. Cao, Guangxi & Xu, Longbing & Cao, Jie, 2012. "Multifractal detrended cross-correlations between the Chinese exchange market and stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4855-4866.
    10. Liu, Li & Wang, Yudong & Wan, Jieqiu, 2010. "Analysis of efficiency for Shenzhen stock market: Evidence from the source of multifractality," International Review of Financial Analysis, Elsevier, vol. 19(4), pages 237-241, September.
    11. Guangxi Cao, 2012. "Time-Varying Effects of Changes in the Interest Rate and the RMB Exchange Rate on the Stock Market of China: Evidence from the Long-Memory TVP-VAR Model," Emerging Markets Finance and Trade, M.E. Sharpe, Inc., vol. 48(0), pages 230-248, July.
    12. Cao, Guangxi & Cao, Jie & Xu, Longbing, 2013. "Asymmetric multifractal scaling behavior in the Chinese stock market: Based on asymmetric MF-DFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 797-807.
    13. Wang, Yudong & Wu, Chongfeng & Wei, Yu, 2011. "Can GARCH-class models capture long memory in WTI crude oil markets?," Economic Modelling, Elsevier, vol. 28(3), pages 921-927, May.
    14. El Alaoui, Marwane & Benbachir, Saâd, 2013. "Multifractal detrended cross-correlation analysis in the MENA area," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5985-5993.
    15. Benbachir, Saâd & El Alaoui, Marwane, 2011. "A Multifractal Detrended Fluctuation Analysis of the Moroccan Stock Exchange," MPRA Paper 49003, University Library of Munich, Germany.
    16. Rizvi, Syed Aun R. & Dewandaru, Ginanjar & Bacha, Obiyathulla I. & Masih, Mansur, 2014. "An analysis of stock market efficiency: Developed vs Islamic stock markets using MF-DFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 86-99.
    17. Li, Muyi & Huang, Yongxiang, 2014. "Hilbert–Huang Transform based multifractal analysis of China stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 222-229.
    18. Wang, Yudong & Wei, Yu & Wu, Chongfeng, 2011. "Analysis of the efficiency and multifractality of gold markets based on multifractal detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(5), pages 817-827.
    19. Chen, Hongtao & Wu, Chongfeng, 2011. "Forecasting volatility in Shanghai and Shenzhen markets based on multifractal analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(16), pages 2926-2935.

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