Oil Price Factors: Forecasting on the Base of Modified Auto-regressive Integrated Moving Average Model

Anthony Nyangarika, Alexey Mikhaylov and Ulf Henning Richter
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Anthony Nyangarika: School of Management and Economics, Beijing Institute of Technology, Beijing, China,
Ulf Henning Richter: School of Economics and Management, Tongji University, Tongji, China.

International Journal of Energy Economics and Policy, 2019, vol. 9, issue 1, 149-159

Abstract: The paper proposes modification of auto-regressive integrated moving average model for finding the parameters of estimation and forecasts using exponential smoothing. The study use data Brent crude oil price and gas prices in the period from January 1991 to December 2016. The result of the study showed an improvement in the accuracy of the predicted values, while the emissions occurred near the end of the time series. It has minimal or no effect on other emissions of this data series. The study suggests that investors can predict prices analyzing the possible risks in oil futures markets.

Keywords: Auto-regressive Integrated Moving Average Model; Econometric Model; Oil Price Forecast (search for similar items in EconPapers)
JEL-codes: C51 C58 F31 G12 G15 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (25)

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Persistent link: https://EconPapers.repec.org/RePEc:eco:journ2:2019-01-19

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