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

Qianjie Geng and Yudong Wang

Energy, 2024, vol. 295, issue C

Abstract: 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.

Keywords: Crude oil market; Futures basis; Volatility forecasting; MCS test; GARCH models (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:295:y:2024:i:c:s0360544224007412

DOI: 10.1016/j.energy.2024.130969

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