Oil Shocks and Volatility Jumps

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 to 2015:02, with the jumps having been computed based on daily data over the same period. Standard linear Granger causality test 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 used a nonparametric causality-in-quantiles test, since the linear model is misspecified. Using this data-driven robust approach, we were able to detect overwhelming evidence of oil shocks predicting volatility jumps of 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 are found to be both qualitatively and quantitatively similar.