| dc.description.abstract | Crude oil is a naturally occurring resource
composed of hydrocarbons and other organic material.
Crude oil price exert a great impact on the global
economy. Therefore, modelling and forecasting crude oil
prices are essential tasks for government policy makers,
investors and even researchers. The objective of this study
is to develop a more accurate time series model for the
monthly crude oil prices. The data consisted of 241
monthly observations of crude oil prices spanning from
April, 1999 to April, 2019. Since the time series of monthly
crude oil prices was non-stationary, the first difference
data set was used where it proved the stationary by both
graphical and theoretical techniques. The best
Autoregressive Integrated Moving Average (ARIMA)
model was selected by using the criteria of Akaike
Information Criterion (AIC), Schwarz Information Criterion
and Hannan-Quinn Information Criterion after testing for
different ARIMA models. Since Auto Regressive
Conditional Heteroscedasticity (ARCH) effect was
presented in the crude oil price time series, a suitable
model was fitted to capture the volatility clustering. The
best model was identified by the lowest AIC values after
testing for various ARCH and GARCH (Generalized ARCH)
models. Hence ARIMA (1, 1, 0) + GARCH (1, 1) was found
to be the best model with lesser root mean squared error
of 4.3017. It can be concluded that the combination of
ARIMA and GARCH models in handling volatility made
hybrid models as the most suitable for analysis and
forecasting crude oil prices | |
