Forecasting Oil Prices Across the Distribution: A Quantile VAR Approach*

Hilde C. Bjørnland (), Nicolás Hardy () and Dimitris Korobilis
Authors registered in the RePEc Author Service: Hilde Christiane Bjørnland ()

No No 03/2026, Working Papers from Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School

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.

Pages: 36 pages
Date: 2026-04
New Economics Papers: this item is included in nep-ene and nep-ets
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https://hdl.handle.net/11250/5506352

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Working Paper: Forecasting Oil Prices Across the Distribution: A Quantile VAR Approach (2026)
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Persistent link: https://EconPapers.repec.org/RePEc:bny:wpaper:0148

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