Forecasting the volatility of crude oil basis: Univariate models versus multivariate models

• Published in: Energy (Oxford) Vol. 295; p. 130969
• Main Authors: Geng, Qianjie, Wang, Yudong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.05.2024
• Subjects: Crude oil market; econometrics; energy; Futures basis; GARCH models; heteroskedasticity; MCS test; oils; petroleum; prices; Volatility forecasting; GARCH models; MCS test; Volatility forecasting; Crude oil market; Futures basis
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2024.130969

The studies on investigating and forecasting crude oil volatility primarily focus on oil price volatility. This research aims to forecast the volatility of the oil futures basis using generalized auto regressive conditional heteroskedasticity (GARCH) and its extended models. Specifically, this study compares the forecasting performance of nine econometric models, including two univariate and seven multivariate models. The empirical results show that simple univariate models outperform the multivariate models in forecasting basis volatility, as indicated by the model confidence set (MCS). Moreover, from an economic perspective, the empirical results also demonstrate that the univariate models generate higher Sharpe ratios than the multivariate ones, consistent with the statistical evaluation results on forecasting accuracy. •We are committed to forecasting the volatility of oil futures basis.•Univariate models display better forecasting performance than multivariate models.•The estimation error has some effects on the more complex multivariate models.•Univariate models have a superior performance from the economic point of view.