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.

Zu Beginn des Jahres 2024 zeigen Stimmungsindikatoren etwas aufgehellte Aussichten für die internationale Konjunktur. In Europa dürfte die Dynamik allerdings recht schwach bleiben. Deutschland befindet sich in einer lang anhaltenden Stagnation, die sich bis zum Sommer fortsetzen wird. Für die Zeit danach ist mit einem leichten Anziehen der Konjunktur zu rechnen. Das Bruttoinlandsprodukt dürfte im Jahr 2024 um lediglich 0,2% expandieren, für 2025 prognostiziert das IWH einen Zuwachs um 1,5%.