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The Application of ARIMA Model in 2014 Shanghai Composite Stock Price Index

Received: 30 June 2015     Accepted: 27 July 2015     Published: 5 August 2015
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Abstract

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.

Published in Science Journal of Applied Mathematics and Statistics (Volume 3, Issue 4)
DOI 10.11648/j.sjams.20150304.16
Page(s) 199-203
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

The Shanghai Composite Stock Price Index (SCSPI), Prediction, ARIMA Model

References
[1] Apergis, N., Mervar, A., & Payne, J. E. (2015). Forecasting disaggregated tourist arrivals in Croatia: evidence from seasonal univariate time series models. Tourism Economics.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] Franke, J., Härdle, W. K., & Hafner, C. M. (2015). ARIMA Time Series Models. In Statistics of Financial Markets (pp. 237-261). Springer Berlin Heidelberg.
[7] 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.
[8] SAS Institute Inc, (2008). SAS/STAT® 9.2 User’s Guide: The ARIMA Procedure (Book Excerpt). NC: SAS Institute Inc, Cary.
[9] Bollerslev T. Generalized autoregressive conditional heteroskedasticity [J]. Journal of Econometrics, 1986, 31 (3): 309-317.
[10] 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.
[11] 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.
[12] Engle R.F. Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation [J]. Econometric, 1982, 50 (4): 989-1004.
[13] Engle R.F., Kroner F.K. Multivariate Simultaneous Generalized ARCH [J].Econometric Theory, 1995, 11:135-149.
[14] 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.
[15] 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.
[16] 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.
[17] Nelsen R.B. An introduction to Copulas [M]. New York: Springer-Verlag, 1999.
[18] Nelson B. Conditional heteroscedasticity in asset returns: a new approach [J]. Econometrica, 1991, 59: 349-360.
[19] Nelson D.B. ARCH models as diffusion approximations [J]. Journal of Econometrics, 1990, 45: 9-28.
[20] 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.
[21] Zakoian J.M. Threshold heteroskedastic models [J]. Journal of Economic Dynamics and Control, 1990, 18: 937-945.
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  • APA Style

    Renhao Jin, Sha Wang, Fang Yan, Jie Zhu. (2015). The Application of ARIMA Model in 2014 Shanghai Composite Stock Price Index. Science Journal of Applied Mathematics and Statistics, 3(4), 199-203. https://doi.org/10.11648/j.sjams.20150304.16

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    ACS Style

    Renhao Jin; Sha Wang; Fang Yan; Jie Zhu. The Application of ARIMA Model in 2014 Shanghai Composite Stock Price Index. Sci. J. Appl. Math. Stat. 2015, 3(4), 199-203. doi: 10.11648/j.sjams.20150304.16

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    AMA Style

    Renhao Jin, Sha Wang, Fang Yan, Jie Zhu. The Application of ARIMA Model in 2014 Shanghai Composite Stock Price Index. Sci J Appl Math Stat. 2015;3(4):199-203. doi: 10.11648/j.sjams.20150304.16

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  • @article{10.11648/j.sjams.20150304.16,
      author = {Renhao Jin and Sha Wang and Fang Yan and Jie Zhu},
      title = {The Application of ARIMA Model in 2014 Shanghai Composite Stock Price Index},
      journal = {Science Journal of Applied Mathematics and Statistics},
      volume = {3},
      number = {4},
      pages = {199-203},
      doi = {10.11648/j.sjams.20150304.16},
      url = {https://doi.org/10.11648/j.sjams.20150304.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20150304.16},
      abstract = {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.},
     year = {2015}
    }
    

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    T1  - The Application of ARIMA Model in 2014 Shanghai Composite Stock Price Index
    AU  - Renhao Jin
    AU  - Sha Wang
    AU  - Fang Yan
    AU  - Jie Zhu
    Y1  - 2015/08/05
    PY  - 2015
    N1  - https://doi.org/10.11648/j.sjams.20150304.16
    DO  - 10.11648/j.sjams.20150304.16
    T2  - Science Journal of Applied Mathematics and Statistics
    JF  - Science Journal of Applied Mathematics and Statistics
    JO  - Science Journal of Applied Mathematics and Statistics
    SP  - 199
    EP  - 203
    PB  - Science Publishing Group
    SN  - 2376-9513
    UR  - https://doi.org/10.11648/j.sjams.20150304.16
    AB  - 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.
    VL  - 3
    IS  - 4
    ER  - 

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Author Information
  • School of Information, Beijing Wuzi University, Beijing, China

  • School of Information, Beijing Wuzi University, Beijing, China

  • School of Information, Beijing Wuzi University, Beijing, China

  • School of Information, Beijing Wuzi University, Beijing, China

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