Browsing by Author "Alexey, Mikhaylov"

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    Energy stability and decarbonization in developing countries: Random Forest approach for forecasting of crude oil trade flows and macro indicators
    (Frontiers in Environmental Science, 2022-11) Nyangarika, Anthony; Alexey, Mikhaylov; Muyeen, S. M.; Yadykin, Vladimir; Mottaeva, Angela; Pryadko, Igor
    The paper observes the dependence of the main macroeconomic indicators in developing countries from the change in world prices for crude oil. We analyzed a system of simultaneous equations, which makes it possible to verify some of these hypotheses, and developed the model to forecast the impact of oil prices on budget revenues. The practical significance of this work lies in the structuring of existing knowledge on the impact of oil crisis. The results of this work can be considered confirmation of the hypothesis of the sensitivity of U.S. macroeconomic indicators to the dynamics of oil prices. Outcomes assume stable growth even in the period of shock prices for oil, which is confirmed by the statistics that were used in the model. Deep decarbonization modeling is a trend in industrial facilities that are used by developing countries. The major challenge is the issue of availability that is applicable to the countries that want to utilize this facility in their communities. Industrial modeling toward decarbonization is now a developing mechanism to curb the growing issue of atmospheric pollution. This paper proves the relevance of promoting deep decarbonization applied by the developing countries.
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    Oil price factors : forecasting on the base of modified auto-regressive integrated moving average model
    (International Journal of Energy Economics and Policy (IJEEP), 2018-11) Nyangarika, Anthony; Ulf Henning, Richter; Alexey, Mikhaylov
    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.