Title :
Stock indices analysis based on ARMA-GARCH model
Author :
Wang, Weiqiang ; Guo, Ying ; Niu, Zhendong ; Cao, Yujuan
Author_Institution :
Sch. of Comput. Sci., Beijing Inst. of Technol., Beijing, China
Abstract :
The generalized autoregressive conditional heteroskedasticity (GARCH) model has become the most popular choice in the analysis of time series datas. In this paper, an autoregressive moving average (ARMA)-GARCH model was built, and it also provided parameter estimation, diagnostic checking procedures to model, and predict Dow and S&P 500 indices data from 1988 to 2008, which extracted from yahoo website, and also compared with the GARCH conventional model, experimental results with both two data sets indicated that this model can be an effective way in financial area.
Keywords :
stock markets; time series; autoregressive moving average model; generalized autoregressive conditional heteroskedasticity model; stock indices analysis; time series datas; yahoo website; Autoregressive processes; Computer science; Data mining; Econometrics; Economic forecasting; Parameter estimation; Predictive models; Stock markets; Technology management; Time series analysis; ARMA-GARCH model; DOW; S&P 500; time series;
Conference_Titel :
Industrial Engineering and Engineering Management, 2009. IEEM 2009. IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-4869-2
Electronic_ISBN :
978-1-4244-4870-8
DOI :
10.1109/IEEM.2009.5373131
