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Predict the Volatility of KLCI Using Stochastic Time Series Models
Muhammad Shuhaifuddin Bin Mohd Shukor
Supervisor: Dr. Hanafi Bin A.Rahim
Bachelor of Science (Financial Mathematics)
School of Informatics and Applied Mathematics
The FTSE Bursa Malaysia Kuala Lumpur Composite Index (KLCI) which comprises of 30
largest listed companies on the main board of Bursa Malaysia is often used as a
benchmark for the overall stock market performance in Malaysia. In this study, the daily
data for the closing prices was used from October 14, 2013 to March 28, 2018 in order
to track the volatility of the daily returns on the KLCI. Next, ARCH effects have been
found to exist in the KLCI daily returns via Lagrange Multiplier test (LM). Thus, this
research was conducted by applying two stochastic time series models, namely GARCH
(1,1) and EGARCH (1,1) methods. Hence, the final result shows that EGARCH (1,1) has
overcomes GARCH (1,1) with the lowest errors detected. Therefore, it can be concluded
that EGARCH (1,1) is a predictive model which is more superior than GARCH (1,1) in
forecasting the volatility of KLCI.
831 | UMT UNDERGRADUATE RESEARCH DAY 2018