Forecasting Oil Prices Across the Distribution: A Quantile VAR Approach

TL;DR

This paper introduces a Quantile Bayesian VAR model to improve oil price forecasts across different risk levels, especially during crises, by capturing distribution asymmetries.

Contribution

The paper develops a novel QBVAR approach that models the entire distribution of oil prices, enhancing tail risk prediction over standard methods.

Findings

Median forecast accuracy improves by 2-5% with QBVAR.

Downside risk predictions improve by 10-25% during crises.

Upside risk remains challenging; stochastic volatility models perform better for it.

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.