An integrated model for crude oil forecasting: Causality assessment and technical efficiency

In light of the central role of crude oil in the economy and the complex mechanisms involved in forecasting crude oil prices, this study proposes a two-stage model that optimally selects driving predictors for crude oil price forecasting by integrating Granger causality test (GCT) and stochastic frontier analysis (SFA). In the first stage, GCT is used to perform causality assessments for 92 predictors across eight categories of factors (demand, supply, inventory, financial market, macroeconomy, economic policy uncertainty, geopolitical risk, and technical indicator). In the second stage, SFA is employed to assess the forecasting power of the preliminarily selected predictors in terms of technical efficiency by using multiple evaluation measures. By collecting a data sample which spans a 21-year period from January 1, 2000 to December 31, 2020, we conduct a comprehensive empirical study by employing rolling time window technique. The empirical results demonstrate that the two-stage model significantly outperforms eight competing models in terms of four forecasting techniques (linear regression, artificial neural network, support vector regression, and random forest). The proposed model's outperformance is robust to different time windows, different forecast horizons, alternative proxies of crude oil prices, and different business conditions. We also explore the time-varying characteristics of predictors for crude oil price forecasting and confirm that financial factors remain vital determinants affecting oil prices.