The Application of ARIMA Model in 2014 Shanghai Composite Stock Price Index
Science Journal of Applied Mathematics and Statistics
Volume 3, Issue 4, August 2015, Pages: 199-203
Received: Jun. 30, 2015; Accepted: Jul. 27, 2015; Published: Aug. 5, 2015
Views 4584      Downloads 146
Renhao Jin, School of Information, Beijing Wuzi University, Beijing, China
Sha Wang, School of Information, Beijing Wuzi University, Beijing, China
Fang Yan, School of Information, Beijing Wuzi University, Beijing, China
Jie Zhu, School of Information, Beijing Wuzi University, Beijing, China
Article Tools
Follow on us
In order to study the changes of Shanghai Composite Stock Price Index (SCSPI) and predict the trend of stock market fluctuations, this paper constructed a time-series analysis.A non-stationary trend is found, and an ARIMA model is found to sufficiently model the data. A short trend of Shanghai composite stock price index is then predicted using the established model.
The Shanghai Composite Stock Price Index (SCSPI), Prediction, ARIMA Model
To cite this article
Renhao Jin, Sha Wang, Fang Yan, Jie Zhu, The Application of ARIMA Model in 2014 Shanghai Composite Stock Price Index, Science Journal of Applied Mathematics and Statistics. Vol. 3, No. 4, 2015, pp. 199-203. doi: 10.11648/j.sjams.20150304.16
Apergis, N., Mervar, A., & Payne, J. E. (2015). Forecasting disaggregated tourist arrivals in Croatia: evidence from seasonal univariate time series models. Tourism Economics.
Akaike, H. (1973), “Information Theory and an Extension of the Maximum Likelihood Principle,” in B.N. Petrov and F.Csaki, ed. 2nd International Symposium on Information Theory, 267-281. Akademia Kiado: Budapest.
Box, G.E.P., Jenkins, G.M., and Reinsel, G.C.(1994), Time Series Analysis: Forecasting and Control, 3rd edition, Prentice Hall: Englewood Cliffs, New Jersey.
Box, G.E.P., and Pierce, D. (1970), “Distribution of Residual Autocorrelations in Auto-regressive-Intergrated Moving Average Time Series Models,” Journal of the American statistical Association, 65, 1509-1526.
Cox, D. R., & Wermuth, N. (1991). A simple approximation for bivariate and trivariate normal integrals. International Statistical Review/Revue Internationale de Statistique, 59(2), 263-269.
Franke, J., Härdle, W. K., & Hafner, C. M. (2015). ARIMA Time Series Models. In Statistics of Financial Markets (pp. 237-261). Springer Berlin Heidelberg.
Tsay, R.S., and Tiao, G.C. (1984), “Consistent Estimates of Auto-regressive Parameters and Extended Sample Auto-correlation Function for Stationary and Non-stationary ARMA models,” Journal of American Statistical Association, 79, 84-96.
SAS Institute Inc, (2008). SAS/STAT® 9.2 User’s Guide: The ARIMA Procedure (Book Excerpt). NC: SAS Institute Inc, Cary.
Bollerslev T. Generalized autoregressive conditional heteroskedasticity [J]. Journal of Econometrics, 1986, 31 (3): 309-317.
Bollerslev T. Modelling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized ARCH Mode [J]. Review of Economics and Statistics,1990, 72: 499-503.
Bollerslev T., Engle R.F., Wooldridge M.J. A capital Asset Pricing Model with time-varying covariances [J]. Journal of Political Economy, 1988, 96: 119-130.
Engle R.F. Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation [J]. Econometric, 1982, 50 (4): 989-1004.
Engle R.F., Kroner F.K. Multivariate Simultaneous Generalized ARCH [J].Econometric Theory, 1995, 11:135-149.
Engle R.F., Lilien D.M., Robins R.P. Estimating time-varying risk Premia in the term structure: The ARCH-M model [J]. Econometrica, 1987, 55: 395-406.
Engle Robert F. Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models [J]. Journal of Business and Economic Statistics, 2002, 20 (3):341-347.
Glosten L. R., Jagannathan R. and Runkle D. E. On the relation between expected value and the volatility of the nominal excess return on stocks [J]. The Journal of Finance, 1993, 48 (5): 1779-1801.
Nelsen R.B. An introduction to Copulas [M]. New York: Springer-Verlag, 1999.
Nelson B. Conditional heteroscedasticity in asset returns: a new approach [J]. Econometrica, 1991, 59: 349-360.
Nelson D.B. ARCH models as diffusion approximations [J]. Journal of Econometrics, 1990, 45: 9-28.
Wang, W. C., Chau, K. W., Xu, D. M., & Chen, X. Y. (2015). Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition. Water Resources Management, 29(8), 2655-2675.
Zakoian J.M. Threshold heteroskedastic models [J]. Journal of Economic Dynamics and Control, 1990, 18: 937-945.
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186