Research on Forecasting the Shanghai and Shenzhen 300 Index Based on the ARIMA-GARCH Model
DOI:
https://doi.org/10.54097/fbem.v10i3.11211Keywords:
ARIMA model, GARCH model, Shanghai and Shenzhen 300 Index, short-term forecasting, stationarity test.Abstract
This paper aims to forecast the closing prices of the Shanghai and Shenzhen 300 Index using time series models. By combining the ARIMA and GARCH models, the study aims to improve the accuracy of short-term predictions for the index and enhance risk management capabilities. The research begins by collecting and preprocessing historical data of the Shanghai and Shenzhen 300 Index. Then, an ARIMA-GARCH model is established, and the model parameters are estimated and tested. Finally, the model is used to forecast the closing prices of the index, and the accuracy of the predictions is evaluated. The results demonstrate that the constructed ARIMA-GARCH model can provide accurate predictions for the closing prices of the Shanghai and Shenzhen 300 Index. This research is expected to enhance prediction accuracy, improve risk management, increase practical value, and serve as a reference for similar studies.
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Zha, Z. (1999). Statistical analysis and forecasting of the Shanghai Stock Composite Index. Journal of Shanghai Maritime University, 04:82-89.
Hui, X., Liu, H., Hu, W., et al. (2003). Forecasting RMB-USD exchange rate based on time series GARCH model. Financial Research, 05, 99-105.
Liu, Y., Xing, W., Ding, L., et al. (2012). Spot electricity price forecasting based on ARIMA-GARCH model. Energy Technology and Economics, 24(02), 59-63.
Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
Ding, Z., Granger, C. W., & Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of empirical finance, 1(1), 83-106.
Mohammadi, H., & Su, L. (2010). International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models. Energy Economics, 32(5), 1001-1008.
Chhorn, T. (2018). Tourism demand and exogenous exchange rate in Cambodia: A stochastic seasonal arimax approach. Theoretical and Practical Research in Economic Fields (TPREF), 9(17), 5-16.
Zhou, B., He, D., & Sun, Z. (2006). Traffic modeling and prediction using ARIMA/GARCH model. In: Modeling and Simulation Tools for Emerging Telecommunication Networks, Springer. pp.101-121.
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