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

^*Corresponding author for this work

School of Computer Science and Technology

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

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	https://doi.org/10.1109/IEEM.2009.5373131
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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

ARMA-GARCH model
DOW
S&P 500
Time series

Access to Document

10.1109/IEEM.2009.5373131

Cite this

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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. in IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management., 5373131, IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management, pp. 2143-2147, IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2009, Hong Kong, China, 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 et al.
IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management. 2009. p. 2143-2147 5373131 (IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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