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The long-range dependence phenomena in asset returns: the Chinese case

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  • Daniel Cajueiro
  • Benjamin Tabak

Abstract

This paper studies the segmented structure of the Chinese stock market, which is a unique opportunity to investigate the possible sources of the long-range dependence phenomena in asset returns. Using the Hurst's exponent evaluated by the Local Whittle method as the measure of long-range dependence, evidence is found supporting that while type B shares present strong evidence of the long-range dependence phenomena, type A shares present only weak evidence of such dependence. This result suggests that liquidity and information transmission play a role in explaining results of market efficiency tests.

Suggested Citation

  • Daniel Cajueiro & Benjamin Tabak, 2006. "The long-range dependence phenomena in asset returns: the Chinese case," Applied Economics Letters, Taylor & Francis Journals, vol. 13(2), pages 131-133.
    • Handle: RePEc:taf:apeclt:v:13:y:2006:i:2:p:131-133
    DOI: 10.1080/13504850500392214
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    References listed on IDEAS

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    1. Cajueiro, Daniel O & Tabak, Benjamin M, 2004. "The Hurst exponent over time: testing the assertion that emerging markets are becoming more efficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 521-537.
    2. Benjamin Miranda Tabak, 2003. "The random walk hypothesis and the behaviour of foreign capital portfolio flows: the Brazilian stock market case," Applied Financial Economics, Taylor & Francis Journals, vol. 13(5), pages 369-378.
    3. Matteo, T. Di & Aste, T. & Dacorogna, Michel M., 2005. "Long-term memories of developed and emerging markets: Using the scaling analysis to characterize their stage of development," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 827-851, April.
    4. Sun, Qian & Tong, Wilson H. S., 2000. "The effect of market segmentation on stock prices: The China syndrome," Journal of Banking & Finance, Elsevier, vol. 24(12), pages 1875-1902, December.
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    Cited by:

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    2. Tan, Pei P. & Galagedera, Don U.A. & Maharaj, Elizabeth A., 2012. "A wavelet based investigation of long memory in stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2330-2341.
    3. Fifield, Suzanne G.M. & Jetty, Juliana, 2008. "Further evidence on the efficiency of the Chinese stock markets: A note," Research in International Business and Finance, Elsevier, vol. 22(3), pages 351-361, September.
    4. Korkmaz, Turhan & Cevik, Emrah Ismail & Özataç, Nesrin, 2009. "Testing for long memory in ISE using Arfima-Figarch model and structural break test," MPRA Paper 71302, University Library of Munich, Germany.
    5. 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.
    6. Cevik, Emrah Ismail, 2012. "İstanbul Menkul Kıymetler Borsası’nda etkin piyasa hipotezinin uzun hafıza modelleri ile analizi: sektörel bazda bir inceleme [The testing of efficient market hypothesis in the Istanbul Stock Excha," MPRA Paper 71484, University Library of Munich, Germany, revised 2012.
    7. Bariviera, Aurelio Fernández, 2011. "The influence of liquidity on informational efficiency: The case of the Thai Stock Market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4426-4432.
    8. Ezzat, Hassan, 2013. "Long Memory Processes and Structural Breaks in Stock Returns and Volatility: Evidence from the Egyptian Exchange," MPRA Paper 51465, University Library of Munich, Germany.
    9. Tzouras, Spilios & Anagnostopoulos, Christoforos & McCoy, Emma, 2015. "Financial time series modeling using the Hurst exponent," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 425(C), pages 50-68.
    10. Hung, Jui-Cheng, 2009. "Deregulation and liberalization of the Chinese stock market and the improvement of market efficiency," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(3), pages 843-857, August.
    11. Zuochao Zhang & Yongjie Zhang & Dehua Shen & Wei Zhang, 2018. "The Dynamic Cross-Correlations between Mass Media News, New Media News, and Stock Returns," Complexity, Hindawi, vol. 2018, pages 1-11, February.
    12. Sensoy, A., 2013. "Time-varying long range dependence in market returns of FEAS members," Chaos, Solitons & Fractals, Elsevier, vol. 53(C), pages 39-45.
    13. Aurelio F. Bariviera & M. Belen Guercio & Lisana B. Martinez & Osvaldo A. Rosso, 2015. "A permutation Information Theory tour through different interest rate maturities: the Libor case," Papers 1509.00217, arXiv.org.
    14. Anagnostidis, Panagiotis & Emmanouilides, Christos J., 2015. "Nonlinearity in high-frequency stock returns: Evidence from the Athens Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 473-487.

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