Annals of Operations Research

, Volume 87, Issue 0, pp 141–152 | Cite as

Modeling Shanghai stock market volatility



There is considerable quantitative research on stock market volatility internationally, but little on China's emerging stock markets. Using Shanghai daily stock return data, this paper studies models for stock market volatility by comparing GARCH, EGARCH and GJR‐GARCHmodels. We find that the GARCH model that accounts for time varying volatility is a suitable model.


Stock Market Stock Return Conditional Variance GARCH Model China Security Regulatory Commission 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    E.R. Berndt, B.H. Hall, R.E. Hall and J.A. Hausanan, Estimation and inference in nonlinear structural models, Annals of Economic and Social Measurement 4(1974)653-665.Google Scholar
  2. [2]
    F. Black, Studies of stock price volatility changes, Proceedings of the American Statistical Association, Business and Economic Statistics Section (1976)177-181.Google Scholar
  3. [3]
    T. Bollerslev, Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics 31(1986) 307-327.CrossRefGoogle Scholar
  4. [4]
    T. Bollerslev, R.Y. Chou and K.F. Kroner, ARCH modelling in finance: A review of the theory and empirical evidence, Journal of Econometrics 52(1992)5-59.CrossRefGoogle Scholar
  5. [5]
    G.E.P. Box and G.M. Jenkins, Time Series Analysis: Forecasting and Control, Holden-Day, 1976.Google Scholar
  6. [6]
    R.F. Engel, Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation, Econometrica 50(1982)987-1007.CrossRefGoogle Scholar
  7. [7]
    R.F. Engel and V.K. Ng, Measuring and testing the impact of news on volatility, Journal of Finance 48(1993)1749-1778.CrossRefGoogle Scholar
  8. [8]
    E.F. Fama, The behavior of stock market prices, Journal of Business 38(1965)34-105.CrossRefGoogle Scholar
  9. [9]
    L.R. Glosten, R. Jagannathan and D.E. Runkle, On the relation between the expected value and the volatility of the nominal excess return on stocks, Journal of Finance 48(1993)1779-1801.CrossRefGoogle Scholar
  10. [10]
    P. Kearns and A.R. Pagan, Australian stock market volatility: 1875-1987, Economic Record 69 (1993)163-178.Google Scholar
  11. [11]
    L.G.M. Ljung and G.E.P. Box, On a measure of lag fit in time series models, Biometrika 67(1978) 297-303.CrossRefGoogle Scholar
  12. [12]
    A. Lo and C. Mackinley, An econometric analysis of non-synchronous trading, Journal of Econometrics 45(1990)181-211.CrossRefGoogle Scholar
  13. [13]
    B. Mandelbrot, The variation of certain speculative prices, Journal of Business 36(1965)394-419.CrossRefGoogle Scholar
  14. [14]
    D.B. Nelson, Conditional heteroskedasticity in asset returns: A new approach, Econometrica 59 (1991)347-370.CrossRefGoogle Scholar
  15. [15]
    A.R. Pagan and H.C.L. Sabau, On the inconsistency of the MLE in certain heteroskedastic regression models, Mimeo, 1987.Google Scholar
  16. [16]
    A.R. Pagan and G.W. Schwert, Alternative models for conditional stock volatility, Journal of Econometrics 45(1990)267-290.CrossRefGoogle Scholar
  17. [17]
    S.-H. Poon and S.J. Taylor, Stock returns and volatility: An empirical study of the UK stock market, Journal of Banking and Finance 16(1992)37-59.CrossRefGoogle Scholar
  18. [18]
    G. Schwarz, Estimating the dimension of a model, Annals of Statistics 6(1978)461-464.Google Scholar
  19. [19]
    J. Xu and W. Chen, Statistical properties of stock returns time series, Foreign Economies and Management (China), suppl. (1996)50-54.Google Scholar
  20. [20]
    J. Xu and G. Tang, The GARCH-M model on return and volatility on the Chinese stock market, Quantitative and Technical Economics (China) 12(1995)28-32Google Scholar

Copyright information

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • J. Xu

There are no affiliations available

Personalised recommendations