Research on the Return Rate of Shenzhen Composite Index in my country
Stock price forecasting plays an important role in judging the trend of my country's stock market. The research object is the daily closing price of Shenzhen Composite Index from January 2, 2015 to December 30, 2020. First, the ARMA (2,4) model is established for the logarithmic return sequence after the first-order difference. Secondly, through analysis, it is concluded that the residual sequence has conditional heteroskedasticity. First, we can try to study the GARCH (1,1) model in depth. Next, a comprehensive ARMA (2,4)-GARCH (1,1) model is constructed, and relevant diagnostic tests are performed. The research results show that the logarithmic return sequence of Shenzhen Composite Index has volatility clustering, and the short-term prediction effect is better than the long-term prediction effect.
References
- Engle R F. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation [J]. Econometrica: Journal of the econometric society, 1982: 987-1007.
- Bollerslev T. Generalized autoregressive conditional heteroskedasticity [J]. Journal of econometrics, 1986, 31(3): 307-327.
- Wan Wei, Jiang Xiaogan. Research on the volatility of my country's Shanghai and Shenzhen stock markets - based on the GARCH family model [J]. Value Engineering, 2007 (10): 14-18.
- Huang Xuan, Zhang Qinglong. Analysis and prediction of volatility of Shanghai and Shenzhen 300 Index based on ARMA-GARCH model [J]. China Prices, 2018, No.350(06): 44-46.
- Li Xiongying, Chen Xiaoling, Zeng Kaihua. Research on stock yield prediction of the four major banks based on three types of models [J]. Economic Mathematics, 2018, 35(04): 21-27. DOI: 10.16339/j.cnki.hdjjsx.2018.04.023.