Forecasting Oil Prices Across the Distribution: A Quantile VAR Approach
Hilde C. Bjornland, Nicolas Hardy, Dimitris Korobilis
TLDR
This paper introduces a Quantile Bayesian VAR (QBVAR) to forecast oil prices across the conditional distribution, improving tail risk assessment.
Key contributions
- QBVAR improves median oil price forecasts by 2-5% over standard Bayesian VARs.
- Uncertainty and financial conditions strongly predict downside risk, improving left-tail forecasts by 10-25%.
- QBVAR significantly enhances tail risk assessment, especially during major oil market disruptions.
- Right-tail forecasting remains challenging, but QBVAR combinations help recover losses.
Why it matters
Standard oil price forecasting misses crucial asymmetries. This QBVAR model provides a more nuanced view by forecasting across the entire conditional distribution. It offers substantial gains for assessing tail risks, which is vital for policymakers and investors during volatile periods.
Original 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.
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