| dc.description.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. | en |
