Oil shocks and volatility jumps

  • Konstantinos GkillasEmail author
  • Rangan Gupta
  • Mark E. Wohar
Original Research


In this paper, we analyse the role of oil price shocks, derived from expectations of consumers, economists, financial market, and policymakers, in predicting volatility jumps in the S&P500 over the monthly period of 1988:01–2015:02, with the jumps having been computed based on daily data over the same period. Standard linear Granger causality tests fail to detect any evidence of oil shocks causing volatility jumps. But given strong evidence of nonlinearity and structural breaks between jumps and oil shocks, we next employed a nonparametric causality-in-quantiles test, as the linear model is misspecified. Using this data-driven robust approach, we were able to detect overwhelming evidence of oil shocks predicting volatility jumps in the S&P500 over its entire conditional distribution, with the strongest effect observed at the lowest considered conditional quantile. Interestingly, the predictive ability of the four oil shocks on volatility jumps is found to be both qualitatively and quantitatively similar.


S&P500 Volatility jumps Oil shocks 

JEL Classification

C22 G10 Q02 



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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Business AdministrationUniversity of PatrasPatrasGreece
  2. 2.Department of EconomicsUniversity of PretoriaPretoriaSouth Africa
  3. 3.College of Business AdministrationUniversity of Nebraska at OmahaOmahaUSA
  4. 4.School of Business and EconomicsLoughborough UniversityLoughboroughUK

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