Analyzing market risk forecasting of asymmetric GARCH model for stock volatility during the Malaysian general election period

Heng, Tan Yu; Xian, Tan Xiao; Cheong, Chin Wen; Khew, Koh Siew; Min, Lim

doi:10.47974/JSMS-1456

Open Access Research Article

Analyzing market risk forecasting of asymmetric GARCH model for stock volatility during the Malaysian general election period

Tan Yu Hengmat2109752@xmu.edu.myDepartment of MathematicsXiamen University MalaysiaSepang, Selangor Darul Ehsan, 43900, MalaysiaView full profile → , Tan Xiao Xianmat2109751@xmu.edu.myDepartment of MathematicsXiamen University MalaysiaSepang, Selangor Darul Ehsan, 43900, MalaysiaView full profile → , ^*Chin Wen CheongCorresponding authorwcchin@xmu.edu.myDepartment of MathematicsXiamen University MalaysiaSepang, Selangor Darul Ehsan, 43900, MalaysiaView full profile → , Koh Siew Khewsiewkhew.koh@xmu.edu.myDepartment of MathematicsXiamen University MalaysiaSepang, Selangor Darul Ehsan, 43900, MalaysiaView full profile → , Lim Minlimmin@xmu.edu.myDepartment of MathematicsXiamen University MalaysiaSepang, Selangor Darul Ehsan, 43900, MalaysiaView full profile →

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pp. 1133–1149Vol. 28Issue 6September 2025DOI: 10.47974/JSMS-1456XML

Received:: 11 Sep 2024
Published Online:: 26 Aug 2025
Article type:: Research Article
Language:: EN
Article no.:: JSMS-1456
Pages:: 1133–1149

Abstract

This study applies the ARMA-GARCH model to analyze the return and volatility of FTSE Bursa Malaysia KLCI, before and after the 14th General Election Malaysia (GE14). On May 9, 2018, this election was the first in Malaysian history to have the reigning party overthrown. In this scenario, we are interested in the effect of the GE14 towards the market index of Malaysia, KLCI. The sample is selected from the period May 10, 2013 until February 20, 2020. Throughout the process of model selection, we found that the ARMA(20,20)-GJR(1,1) model and ARMA(20,20)-GARCH(1,1) model with student’s t-distribution are more appropriate for our dataset. After fitting the appropriate ARMA-GARCH models to our dataset, we perform a 100-period one-day-ahead forecasts and then determine the value-at-risk for a long position of RM 1 million investment in KLCI with different confidence levels.

Keywords

ARMA-GARCH KLCI GE14 Forecasting Value-at-risk

Subject Classifications

91G1591G7062M10

References

[1] A. C. R. Cheian, B. M. Siang, C. L. Woon, C. L. Chiang, and L. C. Hoong, Efficient market hypothesis: Impact of 12th Malaysian general election on the stock market, Final Year Project, Universiti Tunku Abdul Rahman (UTAR), Malaysia (2013).
[2] C. W. Cheong, L. M. Cherng, L. C. Zhi, and Z. Y. Huai, “Do general elections affect fractal structure of stock market?” Journal of Statistics and Management Systems, vol. 24, no. 5, pp. 951–964 (2021), doi: 10.1080/09720510.2020.1776943.
[3] C. L. Choong, S. S. Kun, and T. Y. Theng, A sectorial performance analysis of Kuala Lumpur stock exchange (KLSE Bursa Malaysia), MPRA Paper (2018). [Online]. Available: https://mpra.ub.uni-muenchen.de/
[4] E. Febrian and A. Herwany, “Volatility forecasting models and market cointegration; a study on South-East Asian markets,” The Indonesian Capital Market Review, vol. 1, no. 1, Article 3 (2009).
[5] I. Yunita, “Volatility modeling using Arch/Garch method: Application on Asia Pacific index,” in Proc. 3rd Int. Seminar and Conference on Learning Organization (ISCLO 2015), Atlantis Press, pp. 182–189 (2016).
[6] C. W. Cheong, A. B. S. Mohd Nor, and I. Zaidi, “Asymmetry and long-memory volatility: Some empirical evidence using GARCH,” Physica A: Statistical Mechanics and its Applications, vol. 373, no. 1, pp. 651–664 (2007).
[7] C. W. Cheong, “Time-varying volatility in Malaysian stock exchange: An empirical study using multiple-volatility-shift fractionally integrated model,” Physica A: Statistical Mechanics and its Applications, vol. 387, no. 4, pp. 889–898 (2008).

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