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.
| Original language | English |
|---|---|
| Title of host publication | IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management |
| Pages | 2143-2147 |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - 2009 |
| Event | IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2009 - Hong Kong, China Duration: 8 Dec 2009 → 11 Dec 2009 |
Publication series
| Name | IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management |
|---|
Conference
| Conference | IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2009 |
|---|---|
| Country/Territory | China |
| City | Hong Kong |
| Period | 8/12/09 → 11/12/09 |
Keywords
- ARMA-GARCH model
- DOW
- S&P 500
- Time series
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Wang, W., Guo, Y., Niu, Z., & Cao, Y. (2009). Stock indices analysis based on ARMA-GARCH model. In IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management (pp. 2143-2147). Article 5373131 (IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management). https://doi.org/10.1109/IEEM.2009.5373131