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Oil shocks and volatility jumps

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journal contribution
posted on 2019-03-15, 10:16 authored by Konstantinos Gkillas, Rangan Gupta, Mark Wohar
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.

History

School

  • Business and Economics

Department

  • Business

Published in

Review of Quantitative Finance and Accounting

Volume

54

Issue

1

Pages

247 - 272

Citation

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

© Springer

Version

  • 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-y

Publication date

2019-01-01

ISSN

0924-865X

eISSN

1573-7179

Language

  • en

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