TY - GEN
T1 - Stock indices analysis based on ARMA-GARCH model
AU - Wang, Weiqiang
AU - Guo, Ying
AU - Niu, Zhendong
AU - Cao, Yujuan
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - ARMA-GARCH model
KW - DOW
KW - S&P 500
KW - Time series
UR - http://www.scopus.com/inward/record.url?scp=77949505021&partnerID=8YFLogxK
U2 - 10.1109/IEEM.2009.5373131
DO - 10.1109/IEEM.2009.5373131
M3 - Conference contribution
AN - SCOPUS:77949505021
SN - 9781424448708
T3 - IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management
SP - 2143
EP - 2147
BT - IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management
T2 - IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2009
Y2 - 8 December 2009 through 11 December 2009
ER -
