Evaluating Oil Price Forecasts: A Meta-analysis

Abstract

Oil price forecasts have traditionally attracted the interest of both the empirical literature and policy makers, although research efforts have been intensified in the last 15 years. The present study investigates the forecasting characteristics that have the greatest impact on the accuracy level of such forecasts. To achieve this, we employ a meta-analysis approach of more than 6,000 observations of relative root mean squared errors (RRMSEs) which are pooled within a Bayesian Model Averaging (BMA) method. The findings indicate that forecasting frameworks such as MIDAS and combined forecasts tend to report significantly lower forecast errors. In addition, the choice of the oil price benchmark is an important factor, with the Brent price to offer lower forecast errors. Furthermore, the short-run horizons tend to produce more accurate forecasts and the same holds for the real, instead of the nominal oil prices. A number of robustness tests confirms the validity of these results. Overall, the findings of this study serve as a guide for future oil price forecasting exercises.

A forecasting model for oil prices using a large set of economic indicators

“…The use of an ANN permits capturing nonlinearities, which Goulet Coulombe et al (2022) qualify as a “game changer” for macroeconomic forecasting. Akin to Alquist et al (2013), Baumeister and Kilian (2012), Baumeister et al (2014), and Filippidis et al (2023), among others, our findings indicate that Brent oil price fluctuations exhibit predictability at short horizons relative to the no‐change (or random walk) forecast.…”

Analysis of Stock Portfolio with Global Economic Factors using Dynamic Data Modelling

“…Their study offers a practical approach to forecasting precious metal prices, catering to the interests of investors and analysts in the commodities market. Michail Filippidis et al [12] undertook an evaluation of robust determinants of the WTI/Brent oil price differential, employing dynamic model averaging analysis. Their research contributes to our understanding of the complex factors influencing oil price differentials in the global energy market.…”

Explore the Impact of Macroeconomic Indicators on WTI Oil Prices

“…The interrelationship between WTI and Brent crude oil futures has also been examined, questioning whether expectations or risk premia influence their relative pricing [2]. Similarly, Filippidis, Magkonis, Filis, and Tzouvanas have explored robust determinants of the WTI/Brent oil price differential using dynamic model averaging analysis [3]. Moreover, studies have probed into the socio-political effects on the energy market, particularly the impacts of US political actions on the WTI-Brent spread [4].…”