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Economic recovery in Germany – but structural problems and US trade policy weigh on the economy If the US does not escalate its trade conflicts further, production in Germany…
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Macroeconomic Reports Local and global: IWH regularly provides current economic data - be it about the state of the East German economy, the macroeconomic development in Germany…
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IWH Research Network in Economics (IWH-ReNEc) The Halle Institute for Economic Research (IWH) conducts its research projects in cooperation with external researchers. The IWH…
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IWH Alumni The IWH maintains contact with its former employees worldwide. We involve our alumni in our work and keep them informed, for example, with a newsletter. We also plan…
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People Doctoral Students PhD Representatives Alumni Supervisors Lecturers Coordinators Doctoral Students Afroza Alam (Supervisor: Reint Gropp ) Annika Backes (Supervisors: Simon…
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Centre for Business and Productivity Dynamics (IWH-CBPD) The Centre for Business and Productivity Dynamics (CBPD) was founded in January 2025 and works with policy and research…
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Research Data Centre (IWH-RDC) Direct link to our Data Offer The IWH Research Data Centre offers external researchers access to microdata and micro-aggregated data sets that…
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Internationalisation
Internationalisation The Leibniz Institute for Economic Research Halle (IWH) is responsible for economic research and economic policy advice on a scientific basis. The institute…
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Risky Oil: It's All in the Tails
Christiane Baumeister, Florian Huber, Massimiliano Marcellino
NBER Working Paper,
No. 32524,
2024
Abstract
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.
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IWH FDI Micro Database
IWH FDI Micro Database The IWH FDI Micro Database (FDI = Foreign Direct Investment) comprises a total population of affiliates of multinational enterprises (MNEs) in selected…
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