FORECASTING COCOA BEAN PRICES USING UNIVARIATE TIME SERIES MODELS

Abstract

The purpose of this study is to compare the forecasting performances of different time series methods
for forecasting cocoa bean prices. The monthly average data of Bagan Datoh cocoa bean prices
graded SMC 1B for the period of January 1992 - December 2006 was used. Four different types of
univariate time series methods or models were compared, namely the exponential smoothing,
autoregressive integrated moving average (ARIMA), generalized autoregressive conditional
heteroskedasticity (GARCH) and the mixed ARIMA/GARCH models. Root mean squared error
(RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and Theil's inequality
coefficient (U-STATISTICS) were used as the selection criteria to determine the best forecasting
model. This study revealed that the time series data were influenced by a positive linear trend factor
while a regression test result showed the non-existence of seasonal factors. Moreover, the
Autocorrelation function (ACF) and the Augmented Dickey-Fuller (ADF) tests have shown that the
time series data was not stationary but became stationary after the first order of the differentiating
process was carried out. Based on the results of the ex-post forecasting (starting from January until
December 2006), the mixed ARIMA/GARCH model outperformed the exponential smoothing,
ARIMA, and GARCH models.