Application of ARIMA and GARCH Models in Forecasting Pump Oil Prices in Kenya

Abstract

Oil is an important energy commodity to mankind. Several causes have made oil prices to be volatile. The fluctuation of oil prices has affected many related sectors and stock market indices. Hence, forecasting the pump oil prices is essential to avoid the future prices of the non-renewable natural resources to raise sky-rocket. In this study, average monthly pump oil prices in Kenya for Motor Gasoline and Diesel oil data is obtained from Kenya National Bureau of statistics from January 2000 to May 201l. In the study data for Diesel pump oil prices has been used to come up with suitable models. We use the Box-Jenkins methodology and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) approach in forecasting the Diesel pump oil prices. An Autoregressive Integrated Moving Average (ARIMA) model is set as the benchmark model. We found ARIMA(2,1, 1) and GARCH(1, 1) are the appropriate models under model identification, parameter estimation, diagnostic checking and forecasting future prices. In this study, the analyses are done with the aid of R. Finally, using several measures, comparison performances between ARIMA(2,1, 1) and GARCH(1, 1) models are made. GARCH(1, 1) is found to be a better model than ARIMA(2, 1,1) model. Based on the study, we conclude that ARIMA(2,1,1) model is able to produce accurate forecast based on a description of history patterns in Diesel pump oil prices. However, the GARCH( 1,1) is the better model for monthly Diesel pump oil prices due to its ability to

capture the volatility by the non-constant of coiiditio~al variance.