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Predictability of multifractal analysis of Hang Seng stock index in Hong Kong

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  • Sun, Xia
  • Chen, Huiping
  • Yuan, Yongzhuang
  • Wu, Ziqin
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    Abstract

    In this paper, the daily Hang Seng index in Hong Kong stock market is studied by multifractal analysis. The main parameter of multifractal spectra used is Δf, which can be used to characterize the ratio of number of highest index moments to that of lowest ones. The dependence of today's gain probability (G%) and the day's index increase probability (n%) with Δf of the previous 3 days are studied. It is found that G% and n% can reach 70–80% at the large positive Δf region and can be very close to 20% at the big negative Δf region. The predictability decreases with the increasing number of the previous days.

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    Bibliographic Info

    Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

    Volume (Year): 301 (2001)
    Issue (Month): 1 ()
    Pages: 473-482

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    Handle: RePEc:eee:phsmap:v:301:y:2001:i:1:p:473-482

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    Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/

    Related research

    Keywords: Predictability; Multifractal; Hong Kong index;

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    Cited by:
    1. 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.
    2. Jiang, Zhi-Qiang & Zhou, Wei-Xing, 2008. "Multifractality in stock indexes: Fact or Fiction?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3605-3614.
    3. 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.
    4. Wei, Yu & Chen, Wang & Lin, Yu, 2013. "Measuring daily Value-at-Risk of SSEC index: A new approach based on multifractal analysis and extreme value theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2163-2174.
    5. Siokis, Fotios M., 2013. "Multifractal analysis of stock exchange crashes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1164-1171.
    6. Bai, Man-Ying & Zhu, Hai-Bo, 2010. "Power law and multiscaling properties of the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(9), pages 1883-1890.
    7. Eder Lucio Fonseca & Fernando F. Ferreira & Paulsamy Muruganandam & Hilda A. Cerdeira, 2012. "Identifying financial crises in real time," Papers 1204.3136, arXiv.org, revised Nov 2012.
    8. Du, Guoxiong & Ning, Xuanxi, 2008. "Multifractal properties of Chinese stock market in Shanghai," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(1), pages 261-269.
    9. Cajueiro, Daniel O. & Gogas, Periklis & Tabak, Benjamin M., 2009. "Does financial market liberalization increase the degree of market efficiency? The case of the Athens stock exchange," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 50-57, March.
    10. Chen, Wang & Wei, Yu & Lang, Qiaoqi & Lin, Yu & Liu, Maojuan, 2014. "Financial market volatility and contagion effect: A copula–multifractal volatility approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 289-300.
    11. Rypdal, Martin & Sirnes, Espen & Løvsletten, Ola & Rypdal, Kristoffer, 2013. "Assessing market uncertainty by means of a time-varying intermittency parameter for asset price fluctuations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(16), pages 3335-3343.
    12. Jiang, Zhi-Qiang & Zhou, Wei-Xing, 2008. "Multifractal analysis of Chinese stock volatilities based on the partition function approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(19), pages 4881-4888.
    13. Yuan, Ying & Zhuang, Xin-tian & Jin, Xiu, 2009. "Measuring multifractality of stock price fluctuation using multifractal detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(11), pages 2189-2197.
    14. Yuan, Ying & Zhuang, Xin-tian, 2008. "Multifractal description of stock price index fluctuation using a quadratic function fitting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(2), pages 511-518.
    15. Cajueiro, Daniel O. & Tabak, Benjamin M., 2006. "Testing for predictability in equity returns for European transition markets," Economic Systems, Elsevier, vol. 30(1), pages 56-78, March.
    16. da Fonseca, Eder Lucio & Ferreira, Fernando F. & Muruganandam, Paulsamy & Cerdeira, Hilda A., 2013. "Identifying financial crises in real time," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1386-1392.

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