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

By Vasilios Plakandaras, Rangan Gupta, Wing-Keung Wong. Resources policy, 2019

136 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/96da3dd929da1a1899bdba422c7c07dc56c42090

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In Economics and Econometrics

1. Chao Liang
2. Xiafei Li
3. Yifeng Zhang
4. Xuhui Zhang
5. Yu Wei
6. Yifei Cai
7. Yanrui Wu
8. G. Liang

In Renewable Energy, Sustainability and the Environment

1. Yu Wei
2. Yifei Cai
3. Yanrui Wu

Citations in the corpus

• Uncertainty and crude oil market volatility: new evidence (2020, background): “Besides, various studies have explored the relationship of the geopolitical risk (GPR) index proposed by Caldara and Iacoviello (2018) and oil volatility (e.g. Demirer et al. 2018; Plakandaras, Gupta, and Wong 2019; Smales 2019).”
• Time-varying interactions between geopolitical risks and renewable energy consumption (2021, methodology): “To examine the dynamic interactions, we use two lags for estimation, as used in Primiceri (2005), Franta, Horváth, and Rusnak (2014), and Cai and Menegaki (2019). We use the entire sample period from 1985Q1 to 2018Q2 to derive the priors of the model (Canova, Gambetti, and Pappa 2007).4 By consideri”