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

Authors

  • Anthony Nyangarika School of Management and Economics, Beijing Institute of Technology
  • Alexey Mikhaylov Financial University under the Government of the Russian Federation
  • Ulf Henning Richter

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 ForecastJEL Classifications: C51, C58, F31, G12, G15DOI: https://doi.org/10.32479/ijeep.6812

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Published

2018-12-07

How to Cite

Nyangarika, A., Mikhaylov, A., & Richter, U. H. (2018). Oil Price Factors: Forecasting on the Base of Modified Auto-regressive Integrated Moving Average Model. International Journal of Energy Economics and Policy, 9(1), 149–159. Retrieved from https://econjournals.com./index.php/ijeep/article/view/6812

Issue

Vol. 9 No. 1 (2019)

Section

Articles

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