Comparative Study on Forecasting Crude Palm Oil Price using Time Series Models

Abstract

Forecasting crude palm oil price is important, particularly when the investors encounter with the increasing risks and uncertainties in the future. Therefore, the aim of this study is to forecast the price of palm oil in Malaysia for the next years based on price for the period of 31 years. The objective of the research is to propose an appropriate model to forecast the CPO price. Thus, this study proposes three types of models, which are namely: Autoregressive Integrated Moving Average (ARIMA), Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH). Akaike Information Criterion (AIC) and Hannan-Quinn Criterion (H-Q) statistic were used to obtain the best model. It was found that ARIMA (2, 1, 5) performed better compared to ARCH and GARCH models. It is concluded that ARIMA (2, 1, 5) can be used as an alternative model to forecast the CPO price.