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Conditional heteroscedasticity with leverage effect in stock returns: Evidence from the Chinese stock market

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  • Long, Ling
  • Tsui, Albert K.
  • Zhang, Zhaoyong

Abstract

In recent years the Chinese stock market has experienced an astonishing growth and unprecedented development, but is also viewed as one of the most volatile markets, which has been called by many observers a “casino”. This study intends to examine the presence of heteroskedasticity and the leverage effect in the Chinese stock markets, and to capture the dynamics of conditional correlation between returns of China's stock markets and those of the U.S. in a bivariate VC-MGARCH framework. The results show that the leverage effect is significant in these markets during the sample period in 2000–2013, and the conditional correlation between mainland China's and the U.S. stock markets is quite low and highly volatile. The Chinese stock markets are found to be highly regimes persistent. These findings have important implication for investors seeking opportunity of portfolio diversification.

Suggested Citation

  • Long, Ling & Tsui, Albert K. & Zhang, Zhaoyong, 2014. "Conditional heteroscedasticity with leverage effect in stock returns: Evidence from the Chinese stock market," Economic Modelling, Elsevier, vol. 37(C), pages 89-102.
  • Handle: RePEc:eee:ecmode:v:37:y:2014:i:c:p:89-102
    DOI: 10.1016/j.econmod.2013.11.002
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    References listed on IDEAS

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    1. McAleer, Michael & Chan, Felix & Marinova, Dora, 2007. "An econometric analysis of asymmetric volatility: Theory and application to patents," Journal of Econometrics, Elsevier, vol. 139(2), pages 259-284, August.
    2. Zhuo Qiao & Weiwei Qiao & Wing-Keung Wong, 2011. "Examining the Day-of-the-Week Effects in Chinese Stock Markets: New Evidence from a Stochastic Dominance Approach," Global Economic Review, Taylor & Francis Journals, vol. 40(3), pages 251-267, September.
    3. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(01), pages 122-150, February.
    4. Laurence Copeland & Biqiong Zhang, 2003. "Volatility and Volume in Chinese Stock Markets," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 1(3), pages 287-300.
    5. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    6. 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.
    7. Kim, Chang-Jin, 1993. "Sources of Monetary Growth Uncertainty and Economic Activity: The Time-Varying-Parameter Model with Heteroskedastic Disturbances," The Review of Economics and Statistics, MIT Press, vol. 75(3), pages 483-492, August.
    8. Hwang, Eugene & Min, Hong-Ghi & Kim, Bong-Han & Kim, Hyeongwoo, 2013. "Determinants of stock market comovements among US and emerging economies during the US financial crisis," Economic Modelling, Elsevier, vol. 35(C), pages 338-348.
    9. Doyle, John R. & Chen, Catherine Huirong, 2009. "The wandering weekday effect in major stock markets," Journal of Banking & Finance, Elsevier, vol. 33(8), pages 1388-1399, August.
    10. Bing Zhang & Xindan Li, 2008. "The asymmetric behaviour of stock returns and volatilities: evidence from Chinese stock market," Applied Economics Letters, Taylor & Francis Journals, vol. 15(12), pages 959-962.
    11. J. Xu, 1999. "Modeling Shanghai stock market volatility," Annals of Operations Research, Springer, vol. 87(0), pages 141-152, April.
    12. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    13. Yeh, Yin-Hua & Lee, Tsun-Siou, 2000. "The interaction and volatility asymmetry of unexpected returns in the greater China stock markets," Global Finance Journal, Elsevier, vol. 11(1-2), pages 129-149.
    14. Lin, Xiaoqiang & Fei, Fangyu, 2013. "Long memory revisit in Chinese stock markets: Based on GARCH-class models and multiscale analysis," Economic Modelling, Elsevier, vol. 31(C), pages 265-275.
    15. McAleer, Michael, 2005. "Automated Inference And Learning In Modeling Financial Volatility," Econometric Theory, Cambridge University Press, vol. 21(01), pages 232-261, February.
    16. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. " On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    17. Girardin, Eric & Joyeux, Roselyne, 2013. "Macro fundamentals as a source of stock market volatility in China: A GARCH-MIDAS approach," Economic Modelling, Elsevier, vol. 34(C), pages 59-68.
    18. Johansson, Anders C. & Ljungwall, Christer, 2009. "Spillover Effects Among the Greater China Stock Markets," World Development, Elsevier, vol. 37(4), pages 839-851, April.
    19. Gyu-Hyen Moon & Wei-Choun Yu, 2010. "Volatility Spillovers between the US and China Stock Markets: Structural Break Test with Symmetric and Asymmetric GARCH Approaches," Global Economic Review, Taylor & Francis Journals, vol. 39(2), pages 129-149.
    20. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    21. Li, W K & Ling, Shiqing & McAleer, Michael, 2002. " Recent Theoretical Results for Time Series Models with GARCH Errors," Journal of Economic Surveys, Wiley Blackwell, vol. 16(3), pages 245-269, July.
    22. Hong Li, 2007. "International linkages of the Chinese stock exchanges: a multivariate GARCH analysis," Applied Financial Economics, Taylor & Francis Journals, vol. 17(4), pages 285-297.
    23. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    24. Tse, Y K & Tsui, Albert K C, 2002. "A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 351-362, July.
    25. Lin, Kuan-Pin & Menkveld, Albert J. & Yang, Zhishu, 2009. "Chinese and world equity markets: A review of the volatilities and correlations in the first fifteen years," China Economic Review, Elsevier, vol. 20(1), pages 29-45, March.
    26. 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.
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    Cited by:

    1. repec:eee:ecmode:v:69:y:2018:i:c:p:220-236 is not listed on IDEAS
    2. Hou, Yang & Li, Steven, 2016. "Information transmission between U.S. and China index futures markets: An asymmetric DCC GARCH approach," Economic Modelling, Elsevier, vol. 52(PB), pages 884-897.
    3. Wang, Xunxiao & Wu, Chongfeng & Xu, Weidong, 2015. "Volatility forecasting: The role of lunch-break returns, overnight returns, trading volume and leverage effects," International Journal of Forecasting, Elsevier, vol. 31(3), pages 609-619.

    More about this item

    Keywords

    Stock return; Chinese stock market; Conditional heteroskedasticity; Leverage effect; Regime persistence;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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