Point and density forecasts of oil returns: The role of geopolitical risks

Vasilios Plakandaras, Rangan Gupta and Wing-Keung Wong

Resources Policy, 2019, vol. 62, issue C, 580-587

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.

Keywords: C22; C32; Q41; Q47; Bayesian VAR; Geopolitical risks; Oil prices; Dynamic model averaging (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (71)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0301420718303921
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Point and Density Forecasts of Oil Returns: The Role of Geopolitical Risks (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:62:y:2019:i:c:p:580-587

DOI: 10.1016/j.resourpol.2018.11.006

Access Statistics for this article

Resources Policy is currently edited by R. G. Eggert

More articles in Resources Policy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().