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Cross correlation， feedback trading and spill over effect between China and Hong Kong stock market
作者：李瑜菲
来源：《商情》2015年第33期
【Abstract】My research is targeted to analyze the returns of two stock market indices （China， Hong Kong） to examine the autocorrelation of each stock market， the cross-correlation and positive feedback trading between the two markets. The research generates conditional volatility of market return， conditional autocorrelation between current and lag-one market returns. Further， The study introduces GARCH-M model to analyze the corresponding conditional volatility， the characteristics of volatility spillovers and feedback trading of the two markets. At last， I separate the statistics into two groups， before and after the financial crisis in 2007 to examine the correlation and feedback trading effect separately.
【Key word】Stock market， autocorrelation， cross Correlation ⅠBackground
As the world largest new stocks market， China Stock Market has greatly impacted the global stock markets volatility. China and Hong Kong stock markets have been revealed to present a close correlation over a period of historical time. Whether the two markets exist autocorrelation and cross-correlation？ Whether the two markets present volatility spillover effect and positive feedback trading behavior？ How about the integration degree of the two stocks market？ All of these issues will be discussed and analyzed in this paper in order to solve the real world problem. ⅡModel Hypothesis
1）Theoretical model of stock yield and feedback trading behavior
The research follows Sentana and Wadhwani（1992） model and separates stock traders into information traders and feedback traders. With refer to information traders， the demand function is . 2）Stock yield and feedback trading Empirical model
In order to test such feedback effect， Sentana and Wadhwani（1992） built GARCH-M model 1. Bivariate GARCH-M model
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2. Correlation analysis model
My research divides the conditional various into two groups， high volatile and low volatile group according to conditional variance median， and build the regression function as follows Smiling Curve analysis ⅢEmpirical analysis
1）Research framework and GARCH-M analysis model estimation method
1.Estimate the conditional variance， conditional autocorrelation coefficient based on the two stock market index return data.
2.Estimate the conditional cross autocorrelation coefficient and the accordingly conditional variance using the modified Bollerslev’s bivariate GARCH-M model.
3.Analyze the spill over effect in the two stock markets from investigating the stock return and volatility based on GARCH-M model
4.Regression Analysis of conditional autocorrelation （cross correlation） coefficient and conditional variance to analyze the feedback trading behavior 5.Compare the results before and after 2007 financial crisis 2）Sample selection
Shanghai Stock Index and Hang Seng Index Sample range： From January，8th 2002 to May，25th 2014. 200 observations in each stock market. The data source is from Wind information finance. Statistic processing tool： EViews 6， STATA Bivariate GARCH-M estimation method is BHHH. 3）General statistic analysis of each index
The descriptive statistics characteristics have been obtained：
4）Conditional autocorrelation and cross correlation coefficient estimation between markets ⅣConclusion and research prospect
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There exist positive feedback trading effect in China market， while the effect from HIS is not obvious
For the future research prospective， 2007 subprime mortgage crisis may influence the feedback trading behavior in two stock markets. Before the crisis， the two markets present a positive feedback trading effect whatever HIS-SSE or SSE-HIS. But after the Crisis， this effect is not obvious. （The result comes from empirical hypothesis， which will be further proved in prospect research in the future.） Reference：
[1]Cutler， DM， JM Poterba and LH Summers.（1990）. Speculative Dynamics and the Role of Feedback Traders. American Economic Review， Papers and Proceedings， 80： 63-68. [2]Longin， F.， and Solnik， B. （1995）.Is the Correlation in International Equity Returns Constant ：1960-1990？ Journal of International Money and Finance， 14： 3-26.
[3]Eun， C. S. and S. Shim. （1989）. International Transmission of Stock Market Movements. Journal of Financial Quantitative Analysis， 4， 241-256.