4.6 Article

Nonlinear dynamic correlation between geopolitical risk and oil prices: A study based on high-frequency data

出版社

ELSEVIER
DOI: 10.1016/j.ribaf.2020.101370

关键词

Oil markets; Geopolitical risk; Nonlinear dynamic correlation; Nonlinear causality tests; High-frequency data

资金

  1. National Natural Science Foundation of China [71633006, 71874210, 71874207, 71974208]
  2. Philosophy and Social Science Foundation of Hunan Province of China [19ZWB45]
  3. Innovation Platform Open Foundation of Education Department of Hunan Province [17K103]
  4. National Social Science Foundation of China [19BJY076]
  5. Natural Science Foundation of Hunan Province [2020JJ5784]
  6. InnovationDriven Project of Central South University [2020CX049]

向作者/读者索取更多资源

This study reveals a complex nonlinear relationship between geopolitical risk (GPR) and oil prices, with a bidirectional nonlinear Granger causality found between GPR and oil volatility. GPR mainly affects oil volatility rather than returns, and has a strong positive correlation with volatility jumps in the oil market.
This study investigates the nonlinear dynamic correlations between geopolitical risk (GPR) and oil prices using nonlinear Granger causality and DCC-MVGARCH methods based on high-frequency data. The relationship between GPR and oil prices is found to have a complex nonlinear relationship rather than a simple linear one. Further, a bidirectional nonlinear Granger causality is found to consistently exist between GPR and oil volatility across different components of realized volatility. In terms of returns, GPR has relatively weak unidirectional nonlinear Granger causation with oil returns. The dynamic correlation analysis shows that GPR mainly affects oil volatility rather than returns. Moreover, GPR mainly affects oil volatility through the jump component of the oil market after the financial crisis, and there is a strong positive correlation between GPR and volatility jumps. Our findings innovatively suggest that GPR can potentially be utilized to improve models of volatility jumps and provide reference for investors and price analysts in oil markets who want to design sensible risk-management strategies.

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