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.

Amiti et al. (2024) seek to answer a very topical and important research question: How much did supply-side disruptions and the tight labor market contribute to the recent surge in inflation? The answer provided by the authors is: about 2 percentage points. To arrive at their answer, the authors use a calibrated two-sector New Keynesian model in which they use three correlated shocks in a perfect-storm type setting. The paper also has an interesting empirical part that provides evidence that the channels emphasized in the theoretical model are at work in the data.