ISSN: 2226-3624
Open access
Modelling and forecasting volatility of a financial time series has been a significant area of research in recent years, owing to the fact that volatility is regarded as an essential notion in many economic and financial applications. Because volatility is not directly observable, financial analysts are especially eager to obtain an accurate estimation of this conditional variance process. As a result, a number of models have been developed that are specifically suited to estimate the conditional volatility of financial instruments, with the most well-known and widely used model being the conditional heteroscedastic models. The objectives of this research are; to model and forecast the volatility and price of the Malaysian stock market; to assess the performance of competing models and to simulate the volatility and price of Malaysian stock market. This research estimates and examines the performance of ARIMA and GARCH family type models, standardized GARCH and GARCH-M for symmetric model and EGARCH and GJR-GARCH for asymmetric model using daily return price data. For GARCH models, two distributions were used which were the normal distribution and the t-distribution. The Malaysian stock market which is Kuala Lumpur Composite Index (KLCI) was studied using daily data over an 11-years period beginning from 1st January 2010 and ending on 31st December 2020. The results showed that the ARIMA method is not suitable in forecasting long term data since the ARCH effect is present. While, the performance of asymmetric GARCH models (GJR-GARCH), especially when the fat-tailed densities are taken into account in the conditional volatility, are better than symmetric GARCH. In addition, the student-t distribution performs better than the normal distribution. Moreover, it was found that the AR (1) GJR-GARCH model provides the best forecast for the Malaysian stock market, KLCI. Thus, it was concluded that the asymmetric AR (1) GJR-GARCH model coupled with the student-t distribution, performed well in modeling KLCI dataset. The forecasting process resulted in three different outcomes where the first one foresees a stationary trend of RM1,825.00 all over the years. While the second forecast indicates a fluctuation of around RM1,825.00 to RM1,625.00 and the last outcome had forecast the best result where the trend shows a positive upward trend of over RM1,850.00 all over the year of 2021.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307 - 327.
Deepika, M. G., Gautam, N., & Rajkumar, M. (2012). Forecasting price and analysing factors influencing the price of gold using ARIMA model and multiple regression analysis.
Engle, R. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50, 987 - 1007.
Gallant, A., Rossi, P., & Tauchen, G. (1992). Stock Prices and Volume. Review of Financial Studies, 5, 199 - 242. doi:https://doi.org/10.1093/rfs/5.2.199
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. The Journal of Finance, 48(5), 1779-1801. doi:10.1111/j.1540-6261.1993.tb05128.x
Guha, B., & Bandyopadhyay, G. (2016). Gold Price Forecasting using ARIMA Model. Journal of Advanced Management Science, 4(2), 117 - 121.
Humala, A., & Rodriguez, G. (2010). Foreign exchange intervention and exchange rate volatility in Peru. Applied Economics Letters, 17(15), 1485-1491. doi:10.1080/13504850903049643
Khair, U., Fahmi, H., Al Hakim, S., & Rahim, R. (2017). Forecasting Error Calculation with Mean Absolute Deviation and Mean Absolute Percentage Error. International Conference on Information and Communication Technology (IconICT), 930. doi:10.1088/1742-6596/930/1/012002
Kingsley, A. (2019). Volatility Modelling using Arch and Garch Models (A Case Study of the Nigerian Stock Exchange). International Journal of Mathematics Trends and Technology (IJMTT), 65(4), 58 - 63. doi:10.14445/22315373/IJMTT-V65I4P511
Nor, M. A., & Shamiri, A. (2007). Modeling and Forecasting Volatility of the Malaysian and the Singaporean Stock Indices using Asymmetric GARCH Models and Non-normal Densities. Malaysian Journal of Mathematical Sciences, 1(1), 83 - 102.
Ng, A. (2000). Volatility spillover effects from Japan and the US to the Pacific-Basin. Journal of International Money and Finance, 19(2), 207 - 233.
Shamiri, A., & Isa, Z. (2009). Modeling and Forecasting Volatility of the Malaysian Stock Markets. Journal of Mathematics and Statistics, 5(3), 234 - 240.
Sharma, S., Agarwal, V., & Yadav, M. (2020). Comparison of linear and non-linear GARCH models for forecasting volatility of select emerging countries. Journal of Advances in Management Research. doi:10.1108/JAMR-07-2020-0152
Wang, P. (2009). Financial Econometrics (2nd ed.). New York: Routledge, Taylor and Francis Group.
Zakoian, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955. doi:10.1016/0165-1889(94)90039-6
In-Text Citation: (Ibrahim & Azmi, 2022)
To Cite this Article: Ibrahim, N. ‘I. N., & Azmi, N. N. K. (2022). Modelling and Forecasting the Volatility and Price of Malaysian Stock Market. International Journal of Academic Research in Economics and Management and Sciences, 11(2), 121–138.
Copyright: © 2022 The Author(s)
Published by Human Resource Management Academic Research Society (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode