merged

Forcasting Volatility of Stock Prices Using Arch and Garch Models
Muhammad Qurzainaen Bin Zulkifli
Supervisor: Dr. Che Mohd Imran Bin Che Taib
Bachelor of Science (Financial Mathematics)
School of Informatics and Applied Mathematics
This study analyzes the forecasting volatility of the stock prices in Malaysia. The daily
data of KPJ berhad are observed starting from January 1, 2010 until December 31, 2015.
Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregressive
Conditional Heteroscedasticity (GARCH) are used to model the variances of the stock
prices. ARCH and GARCH models are also used in getting robust estimation since both of
them required a large amount of data which makes they tend to fit the in-sample-data
but they also tend to fall apart in the out-sample-data. Results found that GARCH
performed better than ARCH model by measuring the statistical performances since the
Mean Absolute Error (MAE), Mean absolute percentage error (MAPE), and Root Mean
Square Error (RMSE) of GARCH is smaller than ARCH model.
828 | UMT UNDERGRADUATE RESEARCH DAY 2018