Stock indices analysis based on ARMA-GARCH model

Weiqiang Wang; Ying Guo; Zhendong Niu; Yujuan Cao

doi:10.1109/IEEM.2009.5373131

Stock indices analysis based on ARMA-GARCH model

Weiqiang Wang^*
, Ying Guo
, Zhendong Niu
, Yujuan Cao

^*此作品的通讯作者

计算机学院

Beijing Institute of Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

3 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management
页	2143-2147
页数	5
DOI	https://doi.org/10.1109/IEEM.2009.5373131
出版状态	已出版 - 2009
活动	IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2009 - Hong Kong, 中国期限: 8 12月 2009 → 11 12月 2009

出版系列

姓名	IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management

会议

会议	IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2009
国家/地区	中国
市	Hong Kong
时期	8/12/09 → 11/12/09

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 9 产业、创新和基础设施

访问文件

10.1109/IEEM.2009.5373131

其它文件与链接

链接到 Scopus 的出版物

引用此

@inproceedings{017f282316f4408dbc153d3a948d34b3,

title = "Stock indices analysis based on ARMA-GARCH model",

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 = "ARMA-GARCH model, DOW, S\&P 500, Time series",

author = "Weiqiang Wang and Ying Guo and Zhendong Niu and Yujuan Cao",

year = "2009",

doi = "10.1109/IEEM.2009.5373131",

language = "English",

isbn = "9781424448708",

series = "IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management",

pages = "2143--2147",

booktitle = "IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management",

note = "IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2009 ; Conference date: 08-12-2009 Through 11-12-2009",

}

Wang, W, Guo, Y, Niu, Z & Cao, Y 2009, Stock indices analysis based on ARMA-GARCH model. 在 IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management., 5373131, IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management, 页码 2143-2147, IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2009, Hong Kong, 中国, 8/12/09. https://doi.org/10.1109/IEEM.2009.5373131

Stock indices analysis based on ARMA-GARCH model. / Wang, Weiqiang; Guo, Ying; Niu, Zhendong 等.
IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management. 2009. 页码 2143-2147 5373131 (IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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 - https://www.scopus.com/pages/publications/77949505021

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 -

Stock indices analysis based on ARMA-GARCH model

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

指纹

引用此