Comparative Performance of Arima and Garch Models in Modelling and Forecasting Volatility of Kuala Lumpur Composite Index

Abstract

Time series is a set of observations in sequence over time. Time series modelling is used to create an applicable model that defines the necessary arrangement of the series by study the previous information of a time series. The past information of a time series is used to generate forecast value for the series. It is well acknowledged that a time series are regularly affected with outliers. Outliers may impact the forecasting where the tendency in parameter estimates created by extreme observation will reduce its effectiveness because the optimum predictor for an Autoregressive Integrated Moving Average (ARIMA) model is determined by its parameters. Thus, the occurrence of extreme observations might have a huge effect on predictions value. Therefore, Generalized Autoregressive Conditional Heteroskedastic (GARCH) model has been used to compare the result obtain from ARIMA model. This study used ARIMA and GARCH to compare the best model for forecasting Kuala Lumpur Composite Index (KLCI) when the outlier exists. The best models of ARIMA and GARCH were evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). It can be concluded that GARCH model performed better compared to Box-Jenkins ARIMA in forecasting KLCI.