IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v389y2010i7p1425-1433.html
   My bibliography  Save this article

Long memory volatility in Chinese stock markets

Author

Listed:
  • Kang, Sang Hoon
  • Cheong, Chongcheul
  • Yoon, Seong-Min

Abstract

In this study, the long memory property in the volatility of Chinese stock markets is examined. For this purpose, we applied two semi-parametric tests (GPH and LW) and the FIGARCH model, to four Chinese market indices: Shanghai A, Shanghai B, Shenzhen A and Shenzhen B. From the results of our analysis, we can conclude that the volatility of Chinese stock markets exhibits long memory features, and that the assumption of non-normality provides better specifications regarding long memory volatility processes.

Suggested Citation

  • Kang, Sang Hoon & Cheong, Chongcheul & Yoon, Seong-Min, 2010. "Long memory volatility in Chinese stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(7), pages 1425-1433.
    • Handle: RePEc:eee:phsmap:v:389:y:2010:i:7:p:1425-1433
    DOI: 10.1016/j.physa.2009.12.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437109010000
    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.2009.12.004?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. Philipp Sibbertsen, 2004. "Long memory in volatilities of German stock returns," Empirical Economics, Springer, vol. 29(3), pages 477-488, September.
    2. Andersen, Torben G & Bollerslev, Tim, 1997. "Heterogeneous Information Arrivals and Return Volatility Dynamics: Uncovering the Long-Run in High Frequency Returns," Journal of Finance, American Finance Association, vol. 52(3), pages 975-1005, July.
    3. Bentes, Sónia R. & Menezes, Rui & Mendes, Diana A., 2008. "Long memory and volatility clustering: Is the empirical evidence consistent across stock markets?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(15), pages 3826-3830.
    4. Mantegna,Rosario N. & Stanley,H. Eugene, 2007. "Introduction to Econophysics," Cambridge Books, Cambridge University Press, number 9780521039871.
    5. Tim Bollerslev & Jonathan H. Wright, 2001. "High-Frequency Data, Frequency Domain Inference, And Volatility Forecasting," The Review of Economics and Statistics, MIT Press, vol. 83(4), pages 596-602, November.
    6. Yanhui Liu & Pierre Cizeau & Martin Meyer & Chung-Kang Peng & H. Eugene Stanley, 1997. "Correlations in Economic Time Series," Papers cond-mat/9706021, arXiv.org.
    7. Lobato, Ignacio N & Savin, N E, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 261-268, July.
    8. Mu, Guo-Hua & Zhou, Wei-Xing, 2008. "Relaxation dynamics of aftershocks after large volatility shocks in the SSEC index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5211-5218.
    9. Tian Qiu & Liang Guo & Guang Chen, 2008. "Scaling and Memory Effect in Volatility Return Interval of the Chinese Stock Market," Papers 0805.2194, arXiv.org.
    10. Sibbertsen, Philipp, 2003. "Log-periodogram estimation of the memory parameter of a long-memory process under trend," Statistics & Probability Letters, Elsevier, vol. 61(3), pages 261-268, February.
    11. Jonathan Wright, 2002. "Log-Periodogram Estimation Of Long Memory Volatility Dependencies With Conditionally Heavy Tailed Returns," Econometric Reviews, Taylor & Francis Journals, vol. 21(4), pages 397-417.
    12. Podobnik, Boris & Fu, Dongfeng & Jagric, Timotej & Grosse, Ivo & Eugene Stanley, H., 2006. "Fractionally integrated process for transition economics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 362(2), pages 465-470.
    13. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    14. 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.
    15. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    16. Dionisio, Andreia & Menezes, Rui & Mendes, Diana A., 2007. "On the integrated behaviour of non-stationary volatility in stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 58-65.
    17. Wen Cheong, Chin & Hassan Shaari Mohd Nor, Abu & Isa, Zaidi, 2007. "Asymmetry and long-memory volatility: Some empirical evidence using GARCH," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 373(C), pages 651-664.
    18. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    19. Cajueiro, Daniel O. & Tabak, Benjamin M., 2005. "Testing for time-varying long-range dependence in volatility for emerging markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 346(3), pages 577-588.
    20. Tang, Ta-Lun & Shieh, Shwu-Jane, 2006. "Long memory in stock index futures markets: A value-at-risk approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 366(C), pages 437-448.
    21. Huimin Chung & William T. Lin & Soushan Wu, 2000. "An Analysis of Long Memory in Volatility for Asian Stock Markets," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 3(03), pages 309-330.
    22. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    23. Lamoureux, Christopher G & Lastrapes, William D, 1990. "Persistence in Variance, Structural Change, and the GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 225-234, April.
    24. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    25. Tabak, Benjamin M. & Cajueiro, Daniel O., 2005. "The long-range dependence behavior of the term structure of interest rates in Japan," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 350(2), pages 418-426.
    26. Cajueiro, Daniel O. & Tabak, Benjamin M., 2007. "Long-range dependence and market structure," Chaos, Solitons & Fractals, Elsevier, vol. 31(4), pages 995-1000.
    27. Kang, Sang Hoon & Yoon, Seong-Min, 2007. "Long memory properties in return and volatility: Evidence from the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 385(2), pages 591-600.
    28. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    29. Jiang, Zhi-Qiang & Chen, Wei & Zhou, Wei-Xing, 2008. "Scaling in the distribution of intertrade durations of Chinese stocks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(23), pages 5818-5825.
    30. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    31. 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.
    32. Granger, Clive W. J. & Ding, Zhuanxin, 1996. "Varieties of long memory models," Journal of Econometrics, Elsevier, vol. 73(1), pages 61-77, July.
    33. Qiu, T. & Guo, L. & Chen, G., 2008. "Scaling and memory effect in volatility return interval of the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(27), pages 6812-6818.
    34. Qiu, T. & Zhong, L.X. & Chen, G. & Wu, X.R., 2009. "Statistical properties of trading volume of Chinese stocks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(12), pages 2427-2434.
    35. Bollerslev, Tim & Wright, Jonathan H., 2000. "Semiparametric estimation of long-memory volatility dependencies: The role of high-frequency data," Journal of Econometrics, Elsevier, vol. 98(1), pages 81-106, September.
    36. Cheong, Chin Wen, 2008. "Time-varying volatility in Malaysian stock exchange: An empirical study using multiple-volatility-shift fractionally integrated model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(4), pages 889-898.
    37. Cheng F. Lee & Gong-meng Chen & Oliver M. Rui, 2001. "Stock Returns And Volatility On China'S Stock Markets," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 24(4), pages 523-543, December.
    38. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    39. Kang, Sang Hoon & Yoon, Seong-Min, 2008. "Long memory features in the high frequency data of the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5189-5196.
    40. oh, Gabjin & Kim, Seunghwan & Eom, Cheoljun, 2008. "Long-term memory and volatility clustering in high-frequency price changes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(5), pages 1247-1254.
    41. Jiang, J. & Li, W. & Cai, X. & Wang, Qiuping A., 2009. "Empirical study of recent Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(9), pages 1893-1907.
    42. Chin, Wen Cheong, 2008. "Heavy-tailed value-at-risk analysis for Malaysian stock exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(16), pages 4285-4298.
    43. Lobato, Ignacio N & Savin, N E, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 280-283, July.
    44. Carbone, A. & Castelli, G. & Stanley, H.E., 2004. "Time-dependent Hurst exponent in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 267-271.
    45. Cajueiro, Daniel O. & Tabak, Benjamin M., 2004. "Evidence of long range dependence in Asian equity markets: the role of liquidity and market restrictions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 342(3), pages 656-664.
    46. Liu, Yanhui & Cizeau, Pierre & Meyer, Martin & Peng, C.-K. & Eugene Stanley, H., 1997. "Correlations in economic time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 245(3), pages 437-440.
    Full references (including those not matched with items on IDEAS)

    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. Kang, Sang Hoon & Cheong, Chongcheul & Yoon, Seong-Min, 2010. "Contemporaneous aggregation and long-memory property of returns and volatility in the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4844-4854.
    2. Kang, Sang Hoon & Cho, Hwan-Gue & Yoon, Seong-Min, 2009. "Modeling sudden volatility changes: Evidence from Japanese and Korean stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3543-3550.
    3. Gil-Alana, Luis A. & Shittu, Olanrewaju I. & Yaya, OlaOluwa S., 2014. "On the persistence and volatility in European, American and Asian stocks bull and bear markets," Journal of International Money and Finance, Elsevier, vol. 40(C), pages 149-162.
    4. Charfeddine, Lanouar & Ajmi, Ahdi Noomen, 2013. "The Tunisian stock market index volatility: Long memory vs. switching regime," Emerging Markets Review, Elsevier, vol. 16(C), pages 170-182.
    5. Lu, Yang K. & Perron, Pierre, 2010. "Modeling and forecasting stock return volatility using a random level shift model," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 138-156, January.
    6. David McMillan & Alan Speight, 2006. "Heterogeneous information flows and intra-day volatility dynamics: evidence from the UK FTSE-100 stock index futures market," Applied Financial Economics, Taylor & Francis Journals, vol. 16(13), pages 959-972.
    7. Alia Afzal & Philipp Sibbertsen, 2021. "Modeling fractional cointegration between high and low stock prices in Asian countries," Empirical Economics, Springer, vol. 60(2), pages 661-682, February.
    8. González-Pla, Francisco & Lovreta, Lidija, 2019. "Persistence in firm’s asset and equity volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    9. Jonathan Wright, 2002. "Log-Periodogram Estimation Of Long Memory Volatility Dependencies With Conditionally Heavy Tailed Returns," Econometric Reviews, Taylor & Francis Journals, vol. 21(4), pages 397-417.
    10. Tripathy, Naliniprava, 2022. "Long memory and volatility persistence across BRICS stock markets," Research in International Business and Finance, Elsevier, vol. 63(C).
    11. Pedro J. F. de Lima & Michelle L. Barnes, 2000. "Modeling Financial Volatility: Extreme Observations, Nonlinearities and Nonstationarities," School of Economics and Public Policy Working Papers 2000-05, University of Adelaide, School of Economics and Public Policy.
    12. Scharth, Marcel & Medeiros, Marcelo C., 2009. "Asymmetric effects and long memory in the volatility of Dow Jones stocks," International Journal of Forecasting, Elsevier, vol. 25(2), pages 304-327.
    13. David McMillan & Mark Wohar, 2011. "Structural breaks in volatility: the case of UK sector returns," Applied Financial Economics, Taylor & Francis Journals, vol. 21(15), pages 1079-1093.
    14. Maheu John, 2005. "Can GARCH Models Capture Long-Range Dependence?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-43, December.
    15. Geoffrey Ngene & Ann Nduati Mungai & Allen K. Lynch, 2018. "Long-Term Dependency Structure and Structural Breaks: Evidence from the U.S. Sector Returns and Volatility," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 21(02), pages 1-38, June.
    16. Gil-Alana, Luis A. & Cunado, Juncal & de Gracia, Fernando Perez, 2013. "Salient features of dependence in daily US stock market indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(15), pages 3198-3212.
    17. Jonathan Dark, 2004. "Long memory in the volatility of the Australian All Ordinaries Index and the Share Price Index futures," Monash Econometrics and Business Statistics Working Papers 5/04, Monash University, Department of Econometrics and Business Statistics.
    18. Morana, Claudio & Beltratti, Andrea, 2004. "Structural change and long-range dependence in volatility of exchange rates: either, neither or both?," Journal of Empirical Finance, Elsevier, vol. 11(5), pages 629-658, December.
    19. McMillan, David G. & Ruiz, Isabel, 2009. "Volatility persistence, long memory and time-varying unconditional mean: Evidence from 10 equity indices," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(2), pages 578-595, May.
    20. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.

    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:389:y:2010:i:7:p:1425-1433. 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.