The oil and coke prices forecast evaluation using the different forecasting scheme
DOI:
https://doi.org/10.22394/1993-7601-2025-80-5-25Keywords:
прогнозирование, псевдовневыборочный прогноз, скользящее среднее, скользящая схема, скользящее окно, рекурсивная схема, оценка качества прогноза, нефть, нефтепродукты, ценыAbstract
Price indicators are often non-stationary stochastic processes. As a result, theoretically, the best forecast is simply the last observed value. In this study, we examine various approaches to constructing pseudo out-of-sample forecasts, using futures prices for oil and petroleum products as examples. We use simple econometric models (including different types of random walks, moving averages, linear trend models, and ARIMA) to compare the predictive performance of recursive and rolling (moving window) forecasting schemes. Our results show that the rolling forecasting scheme outperformed the recursive scheme more frequently. Linear trend models with a 6-year rolling window and ARIMA models with 8-year and 9-year rolling windows prove to be the most broadly applicable models for forecasting oil and petroleum product prices. Finally, we observe that forecast accuracy, on average, deteriorates (relative to a naive last-value forecast) as the forecast horizon increases from 24 months to 60 months.Agencies
The article was written as part of the research work carried out under the State Assignment of the Russian Presidential Academy of National Economy and Public Administration (RANEPA) for 2025.Downloads
References
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Sahiner M. (2022) Forecasting volatility in Asian financial markets: Evidence from recursive and rolling window methods. SN Business & Economics, 2 (10), 157. DOI: 10.1007/s43546-022-00329-9.
Smyl S. (2020). A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting, 36 (1), 75–85. DOI: 10.1016/j.ijforecast.2019.03.017.
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West K. D. (2006). Forecast evaluation. Handbook of Economic Forecasting, 1, 99–134. DOI: 10.1016/s1574-0706(05)01003-7.
Zivot E., Wang J. (2003). Rolling analysis of time series. In: Modeling Financial Time Series with S-Plus, 299–346. Springer. DOI: 10.1007/978-0-387-21763-5_9.
