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Abstract
Crude oil price (COP) data are time-series data that are assessed as having both volatility and heteroscedasticity variance. One of the best models that can be applied to address the heteroscedasticity problem is GARCH (generalized autoregressive conditional heteroscedasticity) model. The purpose of this study is to construct the best-fitted model to forecast daily COP as well as to discuss the prepared recommendation for reducing the impact of daily COP movement. Daily COP data are observed for the last decade, i.e., from 2009 to 2018. The finding with the error of less than 0.0001 is AR (1) – GARCH (1,1). The implementation of the model is applicable for both predicting the next 90 days for the COP and its anticipated impact in the future. Because of the increasing prediction, it is recommended that policymakers convert energy use to renewable energy to reduce the cost of oil use.Keywords: Crude Oil Price, Heteroscedasticity, Subsidy, GARCH ModelJEL Classifications: C5, C53, O4, O42, Q4, Q42DOI: https://doi.org/10.32479/ijeep.9513
Highlights
Crude oil price (COP) is an important indicator that must be precisely and accurately calculated
The fitting of an adequate GARCH (p,q) model to the data will be a central aim of the methodology; the following provides a brief introduction to GARCH (p,q) model, the equations of which will be referred to throughout, before introducing econometric considerations that will be applied in the process
ARIMA Model as the COP data set is stationary, the pattern of autocorrelation is recommended to be tested by computing the residual with the Durbin–Watson Test
Summary
Crude oil price (COP) is an important indicator that must be precisely and accurately calculated. The fact that COP volatility is determined by the demand and supply in market, GDP, activity in capital market and exchange rate (Yousefi and Wirjanto, 2004; Bernabe et al 2004). In their empirical study, Alom and Ritson (2012) reported that the increase of COP has an asymmetric relation on certain fuel prices, which is, the largest consumption that increases indirectly the price of commercial products. Speculators take deep consideration of studying behavior of COP movement, and as a result, it can frequently change their position that affects COP volatility (Bu, 2011) This indicates the need for risk management to further investigate the crude oil data, the forecasting of COP
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