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Oil shocks and volatility jumps
journal contribution
posted on 2019-03-15, 10:16 authored by Konstantinos Gkillas, Rangan Gupta, Mark WoharIn 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.
History
School
- Business and Economics
Department
- Business
Published in
Review of Quantitative Finance and AccountingVolume
54Issue
1Pages
247 - 272Citation
GKILLAS, K., GUPTA, R. and WOHAR, M.E., 2019. Oil shocks and volatility jumps. Review of Quantitative Finance and Accounting, 54 (1), pp.247-272.Publisher
© SpringerVersion
- AM (Accepted Manuscript)
Publisher statement
This is a post-peer-review, pre-copyedit version of an article published in Review of Quantitative Finance and Accounting. The final authenticated version is available online at: https://doi.org/10.1007/s11156-018-00788-yPublication date
2019-01-01ISSN
0924-865XeISSN
1573-7179Publisher version
Language
- en