Suchergebnisse

The article studies the factors affecting formation of oil price forecasts by leading expert and official organizations (International Energy Agency, US Energy Information Administration, World Bank, OPEC, RF Ministry of Economic Development). It is demonstrated that all these forecasts take into account both oil market fundamentals and the current conjuncture, but the significance of these factors differs by agency. Non-linear dependence between forecast accuracy and horizon length is identified. The error is greatest for the projections 6 to 8 years ahead, which may be explained by the mismatch between the linear nature of the forecasts and the actual cyclical oil price dynamics during the last 50 years. Accuracy of short- to medium-term projections by the Ministry of Economic Development is shown to hold a median position among forecasting agencies, with the leading position held by the US Energy Information Administration.

Peak price 2023Q4 at $87.7/bbl (WTI), $92.7/bbl (Brent). WTI forecast below: Source: STEO, 12 Sep 2023. Futures imply slightly lower prices than the STEO. Update, 9/13, noon Pacific: Reader Steve Kopits notes that futures are typically in backwardation, i.e., futures prices are below current spot prices. That is true. However, as I have observed on […]

Oil price forecasting has captured the attention of both researchers and academics because of the unique characteristics of crude oil prices and how they have a big impact on a lot of different parts of the economic value of the product. As a result, most academics use a lot of different ways to predict the future. On the other hand, researchers have a hard time because crude oil prices are very unpredictable and can be affected by many different things. This study uses support vector regression (SVR) with technical indicators as a feature to improve the prediction of the monthly West Texas Intermediate (WTI) price of crude oil. The root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) measure how well the model is working. The RMSE was 1.5456, the MAE was 1.3219, and the MAPE was 1.9173 in the experiment. The results show that WTI crude oil prices are affected by technical indicators and get good performance that outperforms most other models that can be found.

We use oil price forecasts from the Consensus Economic Forecast poll to analyze how forecasters form their expectations. Our findings seem to indicate that the extrapolative as well as the regressive expectation formation hypothesis play a role. Standard measures of forecast accuracy reveal forecasters' underperformance relative to the random walk benchmark. However, this result appears to be biased due to peso problems.