Predictability Of The Klci Price Movement: Evidence From The Time Series Models

Abstract

This study utilises autoregressive integrated moving average (ARIMA) time series models to predict the price movement of the Kuala Lumpur Composite Index (KLCI). ARIMAARCH models, which are ARIMA time series models with GARCH errors (relaxing the normality assumption), are also considered. All fitted models excluding those that exhibit nonstationary autoregressive roots are utilised to generate out-of-sample forecast over the forecast horizons of 1 day, 1 week, 1 month, 3 months, 6 months, 9 months and 1 year