Forecasting Oil Prices Across the Distribution: A Quantile VAR Approach

Hilde Bjørnland (), Nicolas Hardy and Dimitris Korobilis

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

Abstract: We develop a Quantile Bayesian Vector Autoregression (QBVAR) to forecast real oil prices across different quantiles of the conditional distribution. The model allows predictor effects to vary across quantiles, capturing asymmetries that standard mean-focused approaches miss. Using monthly data from 1975 to 2025, we document three findings. First, the QBVAR improves median forecasts by 2-5% relative to Bayesian VARs, demonstrating that quantile-specific dynamics matter even for point prediction. Second, uncertainty and financial condition variables strongly predict downside risk, with left-tail forecast improvements of 10-25% that intensify during crisis episodes. Third, right-tail forecasting remains difficult; stochastic volatility models dominate for upside risk, though forecast combinations that include the QBVAR recover these losses. The results show that modeling the conditional distribution yields substantial gains for tail risk assessment, particularly during major oil market disruptions.

Keywords: oil price forecasting; quantile regression; Bayesian VAR; tail risk; distributional forecasting (search for similar items in EconPapers)
JEL-codes: C32 C53 Q41 Q47 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2026-05
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https://crawford.anu.edu.au/sites/default/files/20 ... _Hardy_Korobilis.pdf (application/pdf)

Related works:
Working Paper: Forecasting Oil Prices Across the Distribution: A Quantile VAR Approach (2026)
Working Paper: Forecasting Oil Prices Across the Distribution: A Quantile VAR Approach* (2026)
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2026-39

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