Abstract
In this paper we analyse the role of a news-based index of geopolitical risks (GPRs), in predicting volatility jumps in the Dow Jones Industrial Average (DJIA) over the monthly period of 1899:01 to 2017:12, with the jumps having been computed based on daily data over the same period. Standard linear Granger causality test failed to detect any evidence of GPRs causing volatility jumps. But given strong evidence of nonlinearity and structural breaks between jumps and GPRs, 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 GPRs predicting volatility jumps of the DJIA over its entire conditional distribution. In addition, a cross-quantilogram analysis shows that what matters most for increases in volatility jumps are relatively higher GPRs than lower values of the same.
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