Syazana Zakaria1, Badrina Nur Yasmin Badrul Azhar1, Intan Nadia Azvilla Maulad Mohamad Rawi1 and Noreha Mohamed Yusof2

1,2Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Negeri Sembilan Branch, Seremban Campus, Negeri Sembilan, Malaysia
2This email address is being protected from spambots. You need JavaScript enabled to view it.



Financial Times Stock Exchange (FTSE) Bursa Malaysia Kuala Lumpur Composite Index (KLCI) is made up of over 30 large companies listed on the Bursa Malaysia Main Market. All FTSE Bursa Malaysia data are calculated and disseminated every 15 seconds in real-time. It is believed that the volatility of the stock market has a negative impact on real economic recovery. This paper aims to describe the underlying structure and the phenomenon of the sequence of observations in the series. The information obtained, can determine the performance of time series model to fit the data series from January 2002 until December 2018. Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have been shown to provide the correct trend of volatility. The objectives of this paper are to determine the overall trend of the KLCI stock return and to investigate the performance of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Autoregressive Integrated Moving Average (ARIMA) based on KLCI stock return. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) have been chosen to be used in this paper to measure accuracy. The results show that the best ARIMA model is ARIMA(1,1), while for the GARCH model, it is GARCH(1,1).

Keywords: Kuala Lumpur Composite Index, ARIMA, GARCH.


Published On: 08 September 2020


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