Stochastic Forecasting and Modeling of Volatility Oil Prices in Ghana using ARIMA Time Series Model

Abstract

Ghana’s demand for crude oil and refined petroleum products has been growing over the past decade. This growth has been driven by socio-economic and technical factors that have influenced each category of final energy use. The growing urban population is demanding new vehicles and new roads, raising the demand for energy in the transportation and all other sectors of the economy. Consequently, Oil prices rose from 2004 to historic highs in mid-2008, only to fall precipitously in the last four months of 2008 and lose all the gains of the preceding four and a half years. The steep price increase was challenging for all economies including Ghana. The high price of oil will invariably affect revenue mobilisation, expenditure, and therefore the fiscal position of government and inflation.

The study is an attempt to forecast and analyse the macroeconomic impact of oil price fluctuations in Ghana using annual data from 2000-2011. It focuses on studying the feasibility forecast using nested conditional mean (ARIMA) and conditional variance (GARCH, GJR, EGARCH) family of models under such volatile market conditions. A regression based forecast filtering simulation is proposed and studied for any improvements in the forecasted results.

Keywords: ARIMA model, stochastic, volatility, forecast, crude oil, price shocks