Forecasting Oil Prices Across the Distribution: A Quantile VAR Approach

Paper: arXiv 2604.12927

Authors: Hilde C. Bjornland, Nicolas Hardy, Dimitris Korobilis

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.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces a novel Quantile Bayesian Vector Autoregression (QBVAR) framework to capture tail asymmetries in oil price forecasting, combining solid Bayesian econometrics with extensive empirical testing over a 50-year dataset (1975-2025). The substantial forecast improvements in left-tail risk demonstrate strong practical and empirical utility for risk management.

Research Flowchart

  flowchart TD
    A[Research Goal: Forecast Real Oil Prices Across Conditional Distribution] --> B[Data Inputs: Monthly Data 1975-2025<br/>Macro, Uncertainty & Financial Variables]
    B --> C[Methodology: Quantile Bayesian VAR - QBVAR]
    C --> D[Computational Process: Model Estimation Across Quantiles & Forecast Combination]
    D --> E1[Outcome 1: Median Forecasts<br/>2-5% improvement over standard BVARs]
    D --> E2[Outcome 2: Downside Risk / Left-Tail<br/>10-25% improvement during crisis episodes]
    D --> E3[Outcome 3: Upside Risk / Right-Tail<br/>Stochastic Volatility dominates; combinations recover losses]

Cited References (64)

1. Risky Oil: I t’s All in the Tails (2024) — link
2. Efficient B ayesian Inference for Multivariate Factor Stochastic Volatility Models (2017) — link
3. Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market (2009)
4. Causes and consequences of the oil shock of 2007-08 (2009)
5. The role of inventories and speculative trading in the global market for crude oil (2014)
6. Measuring geopolitical risk (2022)
7. Credit spreads and business cycle fluctuations (2012)
8. Measuring uncertainty (2015)

0 of 64 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.