The substantial fluctuations in oil prices in the wake of the COVID-19 pandemic and the Russian invasion of Ukraine have highlighted the importance of tail events in the global market for crude oil which call for careful risk assessment. In this paper we focus on forecasting tail risks in the oil market by setting up a general empirical framework that allows for flexible predictive distributions of oil prices that can depart from normality. This model, based on Bayesian additive regression trees, remains agnostic on the functional form of the conditional mean relations and assumes that the shocks are driven by a stochastic volatility model. We show that our nonparametric approach improves in terms of tail forecasts upon three competing models: quantile regressions commonly used for studying tail events, the Bayesian VAR with stochastic volatility, and the simple random walk. We illustrate the practical relevance of our new approach by tracking the evolution of predictive densities during three recent economic and geopolitical crisis episodes, by developing consumer and producer distress indices that signal the build-up of upside and downside price risk, and by conducting a risk scenario analysis for 2024.
Authors
Christiane Baumeister, Florian Huber, Massimiliano Marcellino
- Acknowledgements & Disclosure
- We thank Jim Hamilton for useful suggestions, and participants at the IWEEE 2024 Workshop and the 2024 IIASA Workshop on Agricultural Commodity Prices for helpful comments. Marcellino thanks MUR- Prin 2022- Prot. 20227YZ9JK, financed by the European Union - Next Generation EU, for partial financial support. Huber gratefully acknowledges funding from the Austrian Science Fund: ZK-35. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
- DOI
- https://doi.org/10.3386/w32524
- Published in
- United States of America
Table of Contents
- Introduction 3
- Alternative Approaches to Modeling Tail Risks in Oil Price Dynamics 5
- Bayesian Vector Autoregressions 5
- Bayesian Quantile Regressions 6
- Bayesian Additive Regression Trees 7
- Capturing Heteroskedasticity 8
- The Role of Nonlinearities in Oil Markets 9
- A Comprehensive Set of Economically-Motivated Predictors 9
- An In-Sample Primer on Nonlinear Relationships 10
- (Tail) Forecasting the Real Price of Oil 12
- Forecasting Environment 13
- Forecast Evaluation 14
- Point Forecasts 14
- Tail Forecasts 14
- Tracking the Forecasting Performance over Time 15
- Monitoring Oil Price Risks in Real Time 18
- Real-Time Assessment of Tail Risks 18
- The COVID-19 Pandemic and the Russia-Saudi Oil Price War 21
- The Russian Invasion of Ukraine and the Energy Crisis 21
- The Israel-Hamas War and the Red Sea Crisis 22
- Tail-Risk Indicators for Producers and Consumers 22
- Risk Scenarios for 2024 25
- Conclusions 27
- Illustration of a Tree-Based Model 29
- Prior Specifications and Bayesian Inference 30
- Priors for regression coefficients 31
- Priors for factor loadings 32
- Priors for stochastic volatilities 32
- Priors for trees 32
- Prior for scale parameter at quantile 33
- Bayesian inference 33
- Data Sources and Backcasting 34
- Results for the Full Hold-Out Period 34
