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

fYear

2009

fDate

8-11 Dec. 2009

Firstpage

2143

Lastpage

2147

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/IEEM.2009.5373131

Filename

5373131