Oil shocks and volatility jumps

Konstantinos Gkillas (), Rangan Gupta and Mark Wohar ()
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Konstantinos Gkillas: University of Patras

Review of Quantitative Finance and Accounting, 2020, vol. 54, issue 1, No 8, 247-272

Abstract: Abstract 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.

Keywords: S&P500; Volatility jumps; Oil shocks (search for similar items in EconPapers)
JEL-codes: C22 G10 Q02 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (9)

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Working Paper: Oil Shocks and Volatility Jumps (2018)
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DOI: 10.1007/s11156-018-00788-y

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