New hybrid methodology for stock volatility prediction

Chih Hsiung Tseng; Sheng Tzong Cheng; Yi Hsien Wang

doi:10.1016/j.eswa.2007.12.004

New hybrid methodology for stock volatility prediction

Chih Hsiung Tseng, Sheng Tzong Cheng, Yi Hsien Wang

Department of Computer Science and Information Engineering

Research output: Contribution to journal › Article › peer-review

25 Citations (Scopus)

Abstract

Modeling and forecasting stock market volatility have received considerable attention by both academics and practitioners. Hence, this paper presents integrated model to improve the variance forecasting ability in variance as compared to the traditional GARCH. Overall, the results show that the new integrated model can enhance the volatility forecasting ability of the traditional GARCH.

Original language	English
Pages (from-to)	1833-1839
Number of pages	7
Journal	Expert Systems With Applications
Volume	36
Issue number	2 PART 1
DOIs	https://doi.org/10.1016/j.eswa.2007.12.004
Publication status	Published - 2009 Mar

All Science Journal Classification (ASJC) codes

General Engineering
Computer Science Applications
Artificial Intelligence

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10.1016/j.eswa.2007.12.004

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title = "New hybrid methodology for stock volatility prediction",

abstract = "Modeling and forecasting stock market volatility have received considerable attention by both academics and practitioners. Hence, this paper presents integrated model to improve the variance forecasting ability in variance as compared to the traditional GARCH. Overall, the results show that the new integrated model can enhance the volatility forecasting ability of the traditional GARCH.",

author = "Tseng, {Chih Hsiung} and Cheng, {Sheng Tzong} and Wang, {Yi Hsien}",

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AB - Modeling and forecasting stock market volatility have received considerable attention by both academics and practitioners. Hence, this paper presents integrated model to improve the variance forecasting ability in variance as compared to the traditional GARCH. Overall, the results show that the new integrated model can enhance the volatility forecasting ability of the traditional GARCH.

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JO - Expert Systems With Applications

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New hybrid methodology for stock volatility prediction

Abstract

All Science Journal Classification (ASJC) codes

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