IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v490y2018icp497-503.html
   My bibliography  Save this article

Multifractal property of Chinese stock market in the CSI 800 index based on MF-DFA approach

Author

Listed:
  • Zhu, Huijian
  • Zhang, Weiguo

Abstract

CSI 800 index consists of CSI 500 index and CSI 300 index, aiming to reflect the performance of stocks with large, mid and small size of China A share market. In this paper we analyze the multifractal structure of Chinese stock market in the CSI 800 index based on the multifractal detrended fluctuation analysis (MF-DFA) method. We find that the fluctuation of the closing logarithmic returns have multifractal properties, the shape and width of multifractal spectrum are depended on the weighing order q. More interestingly, we observe a bigger market crash in June–August 2015 than the one in 2008 based on the local Hurst exponents. The result provides important information for further study on dynamic mechanism of return fluctuation and whether it would trigger a new financial crisis.

Suggested Citation

  • Zhu, Huijian & Zhang, Weiguo, 2018. "Multifractal property of Chinese stock market in the CSI 800 index based on MF-DFA approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 497-503.
    • Handle: RePEc:eee:phsmap:v:490:y:2018:i:c:p:497-503
    DOI: 10.1016/j.physa.2017.08.060
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843711730794X
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2017.08.060?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Serrano, E. & Figliola, A., 2009. "Wavelet Leaders: A new method to estimate the multifractal singularity spectra," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(14), pages 2793-2805.
    2. Xavier Gabaix, 2009. "Power Laws in Economics and Finance," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 255-294, May.
    3. Sun, Xia & Chen, Huiping & Yuan, Yongzhuang & Wu, Ziqin, 2001. "Predictability of multifractal analysis of Hang Seng stock index in Hong Kong," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 301(1), pages 473-482.
    4. Onali, Enrico & Goddard, John, 2009. "Unifractality and multifractality in the Italian stock market," International Review of Financial Analysis, Elsevier, vol. 18(4), pages 154-163, September.
    5. Ho, Ding-Shun & Lee, Chung-Kung & Wang, Cheng-Cai & Chuang, Mang, 2004. "Scaling characteristics in the Taiwan stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 332(C), pages 448-460.
    6. 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.
    7. Katsuragi, Hiroaki, 2000. "Evidence of multi-affinity in the Japanese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 278(1), pages 275-281.
    8. He, Xiaoli & Wang, Hongwu & Du, Ziping, 2014. "The complexity and fractal structures of CSI300 before and after the introduction of CSI300IF," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 76-85.
    9. Sun, Xia & Chen, Huiping & Wu, Ziqin & Yuan, Yongzhuang, 2001. "Multifractal analysis of Hang Seng index in Hong Kong stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 291(1), pages 553-562.
    10. Zhou, Yuan-Wu & Liu, Jin-Long & Yu, Zu-Guo & Zhao, Zhi-Qin & Anh, Vo, 2014. "Fractal and complex network analyses of protein molecular dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 21-32.
    11. Wang, Yudong & Liu, Li & Gu, Rongbao, 2009. "Analysis of efficiency for Shenzhen stock market based on multifractal detrended fluctuation analysis," International Review of Financial Analysis, Elsevier, vol. 18(5), pages 271-276, December.
    12. 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.
    13. Costa, Rogério L. & Vasconcelos, G.L., 2003. "Long-range correlations and nonstationarity in the Brazilian stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 329(1), pages 231-248.
    14. R. L. Costa & G. L. Vasconcelos, 2003. "Long-range correlations and nonstationarity in the Brazilian stock market," Papers cond-mat/0302342, arXiv.org.
    15. 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.
    16. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    17. 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.
    18. Kumar, Sunil & Deo, Nivedita, 2009. "Multifractal properties of the Indian financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1593-1602.
    19. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fang, Sheng & Lu, Xinsheng & Li, Jianfeng & Qu, Ling, 2018. "Multifractal detrended cross-correlation analysis of carbon emission allowance and stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 551-566.
    2. Wang, Jian & Shao, Wei & Ma, Chenmin & Chen, Wenbing & Kim, Junseok, 2021. "Co-movements between Shanghai Composite Index and some fund sectors in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    3. Chenyu Han & Yiming Wang & Yingying Xu, 2019. "Efficiency and Multifractality Analysis of the Chinese Stock Market: Evidence from Stock Indices before and after the 2015 Stock Market Crash," Sustainability, MDPI, vol. 11(6), pages 1-15, March.
    4. Ruan, Qingsong & Meng, Lu & Lv, Dayong, 2021. "Effect of introducing Bitcoin futures on the underlying Bitcoin market efficiency: A multifractal analysis," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    5. OlaOluwa S. Yaya & Oluwasegun B. Adekoya & Xuan Vinh Vo & Mamdouh Abdulaziz Saleh Al‐Faryan, 2024. "Stock Market Efficiency in Asia: Evidence from the Narayan–Liu–Westerlund's GARCH‐based unit root test," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 91-101, January.
    6. Miao He & Yanhong Guo, 2022. "Systemic Risk Contributions of Financial Institutions during the Stock Market Crash in China," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    7. Mensi, Walid & Hamdi, Atef & Yoon, Seong-Min, 2018. "Modelling multifractality and efficiency of GCC stock markets using the MF-DFA approach: A comparative analysis of global, regional and Islamic markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1107-1116.
    8. Wang, Qizhen, 2019. "Multifractal characterization of air polluted time series in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 167-180.
    9. Un, Kuok Sin & Ausloos, Marcel, 2022. "Equity premium prediction: Taking into account the role of long, even asymmetric, swings in stock market behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    10. Yaya, OlaOluwa S. & Vo, Xuan Vinh & Adekoya, Oluwasegun B., 2021. "Market Efficiency of Asian Stocks: Evidence based on Narayan-Liu-Westerlund GARCH-based Unit root test," MPRA Paper 109828, University Library of Munich, Germany.
    11. Yan, Ruzhen & Yue, Ding & Chen, Xudong & Wu, Xu, 2020. "Non-linear characterization and trend identification of liquidity in China's new OTC stock market based on multifractal detrended fluctuation analysis," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    12. Ardalankia, Jamshid & Osoolian, Mohammad & Haven, Emmanuel & Jafari, G. Reza, 2020. "Scaling features of price–volume cross correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    13. Han, Chenyu & Wang, Yiming & Ning, Ye, 2019. "Analysis and comparison of the multifractality and efficiency of Chinese stock market: Evidence from dynamics of major indexes in different boards," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 528(C), pages 1-1.
    14. Mensi, Walid & Tiwari, Aviral Kumar & Al-Yahyaee, Khamis Hamed, 2019. "An analysis of the weak form efficiency, multifractality and long memory of global, regional and European stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 168-177.
    15. Li, Yong, 2019. "Multifractal view on China’s stock market crashes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    16. Wang, Lei & Liu, Lutao, 2020. "Long-range correlation and predictability of Chinese stock prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    17. Jamshid Ardalankia & Mohammad Osoolian & Emmanuel Haven & G. Reza Jafari, 2019. "Scaling Features of Price-Volume Cross-Correlation," Papers 1903.01744, arXiv.org, revised Aug 2020.
    18. Mensi, Walid & Lee, Yun-Jung & Vinh Vo, Xuan & Yoon, Seong-Min, 2021. "Does oil price variability affect the long memory and weak form efficiency of stock markets in top oil producers and oil Consumers? Evidence from an asymmetric MF-DFA approach," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    19. Deniz Erer & Elif Erer & Selim Güngör, 2023. "The aggregate and sectoral time-varying market efficiency during crisis periods in Turkey: a comparative analysis with COVID-19 outbreak and the global financial crisis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhou, Wei-Xing, 2012. "Finite-size effect and the components of multifractality in financial volatility," Chaos, Solitons & Fractals, Elsevier, vol. 45(2), pages 147-155.
    2. Gu, Rongbao & Xiong, Wei & Li, Xinjie, 2015. "Does the singular value decomposition entropy have predictive power for stock market? — Evidence from the Shenzhen stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 439(C), pages 103-113.
    3. 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.
    4. Stavroyiannis, S. & Makris, I. & Nikolaidis, V., 2010. "Non-extensive properties, multifractality, and inefficiency degree of the Athens Stock Exchange General Index," International Review of Financial Analysis, Elsevier, vol. 19(1), pages 19-24, January.
    5. Mensi, Walid & Lee, Yun-Jung & Vinh Vo, Xuan & Yoon, Seong-Min, 2021. "Does oil price variability affect the long memory and weak form efficiency of stock markets in top oil producers and oil Consumers? Evidence from an asymmetric MF-DFA approach," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    6. 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.
    7. Zunino, Luciano & Figliola, Alejandra & Tabak, Benjamin M. & Pérez, Darío G. & Garavaglia, Mario & Rosso, Osvaldo A., 2009. "Multifractal structure in Latin-American market indices," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2331-2340.
    8. 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.
    9. Lee, Minhyuk & Song, Jae Wook & Kim, Sondo & Chang, Woojin, 2018. "Asymmetric market efficiency using the index-based asymmetric-MFDFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1278-1294.
    10. 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.
    11. Chenyu Han & Yiming Wang & Yingying Xu, 2019. "Efficiency and Multifractality Analysis of the Chinese Stock Market: Evidence from Stock Indices before and after the 2015 Stock Market Crash," Sustainability, MDPI, vol. 11(6), pages 1-15, March.
    12. Akash P. POOJARI & Siva Kiran GUPTHA & G Raghavender RAJU, 2022. "Multifractal analysis of equities. Evidence from the emerging and frontier banking sectors," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(3(632), A), pages 61-80, Autumn.
    13. Jia, Zhanliang & Cui, Meilan & Li, Handong, 2012. "Research on the relationship between the multifractality and long memory of realized volatility in the SSECI," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(3), pages 740-749.
    14. Yang, Yujun & Li, Jianping & Yang, Yimei, 2017. "The cross-correlation analysis of multi property of stock markets based on MM-DFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 23-33.
    15. Zunino, L. & Tabak, B.M. & Figliola, A. & Pérez, D.G. & Garavaglia, M. & Rosso, O.A., 2008. "A multifractal approach for stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(26), pages 6558-6566.
    16. Wang, Qizhen & Zhu, Yingming & Yang, Liansheng & Mul, Remco A.H., 2017. "Coupling detrended fluctuation analysis of Asian stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 337-350.
    17. 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.
    18. 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.
    19. Lee, Minhyuk & Song, Jae Wook & Park, Ji Hwan & Chang, Woojin, 2017. "Asymmetric multi-fractality in the U.S. stock indices using index-based model of A-MFDFA," Chaos, Solitons & Fractals, Elsevier, vol. 97(C), pages 28-38.
    20. Diniz-Maganini, Natalia & Diniz, Eduardo H. & Rasheed, Abdul A., 2021. "Bitcoin’s price efficiency and safe haven properties during the COVID-19 pandemic: A comparison," Research in International Business and Finance, Elsevier, vol. 58(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:490:y:2018:i:c:p:497-503. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.