Point and density forecasts of oil returns: The role of geopolitical risks. (August 2019)

• Record Type: Journal Article
• Authors: Plakandaras, Vasilios; Gupta, Rangan; Wong, Wing-Keung
• Is Part Of: Resources policy. Volume 62(2019)
• Page Start: 580
• Page End: 587
• Publication Date: 2019-08
• Subjects: C22 -- C32 -- Q41 -- Q47; Bayesian VAR -- Geopolitical risks -- Oil prices -- Dynamic model averaging; Mines and mineral resources -- Periodicals; Ressources minérales -- Périodiques; Ressources naturelles -- Gestion -- Périodiques; Environnement -- Politique gouvernementale -- Périodiques; 333.8
• DOI: 10.1016/j.resourpol.2018.11.006
• Languages: English
• ISSNs: 0301-4207

Abstract

Abstract: We examine the dynamic relationship between oil prices and news-based indices of global geopolitical risks (GPRs), as well as a composite measure of the same for emerging economies, which we develop using Dynamic Model Averaging (DMA). In doing so, we train a number of linear and nonlinear probabilistic models to capture the ability of GPRs in forecasting oil returns. Our empirical findings show that global GPRs associated with wars is the most accurate in forecasting oil returns in the short-run, while composite GPRs emanating from the emerging markets, forecasts oil returns relatively better at medium- to longer-horizons. However, differences across the linear and nonlinear models incorporating information of GPRs are not necessarily markedly different. Given an observe negative relationship between GPRs and oil returns, density forecasts show that increases in GPRs from their initial lower levels, which would imply higher conditional oil returns initially, can predict the resulting increases in oil returns thereafter more accurately compared to the lower end of the conditional distribution, which in turn, corresponds to higher initial levels of GPRs. Highlights: We examine the dynamic relationship between oil prices and geopolitical risk. We use linear and nonlinear probabilistic models. Oil prices are affected by war threats and acts only in the short run. From density forecasts oil prices can be predicted during periods of low risk.