Oil shocks and volatility jumps

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.

Appendix

See Figs. 2, 3, 4, 5, 6, 7, 8, 9, 10 and Tables 3, 4, 5.

Fig. 2

Data plots

Fig. 3

Cross-quantilogram results. Sample cross-quantilograms for $\alpha_{2} = 0.1$ to detect directional predictability from consumers’ oil price shocks to JUMPS. Bar graphs describe sample cross-quantilograms and red lines are the 95% bootstrap confidence intervals for 2000 bootstrap iterations. Note that $\alpha_{1}$, $\alpha_{2}$ correspond to the quantiles of JUMPS and oil shocks respectively

Fig. 4

Cross-quantilogram results. Sample cross-quantilograms for $\alpha_{2} = 0.9$ to detect directional predictability from consumers’ oil price shocks to JUMPS. Bar graphs describe sample cross-quantilograms and red lines are the 95% bootstrap confidence intervals for 2000 bootstrap iterations. Note that $\alpha_{1}$, $\alpha_{2}$ correspond to the quantiles of JUMPS and oil shocks respectively

Fig. 5

Cross-quantilogram results. Sample cross-quantilograms for $\alpha_{2} = 0.1$ to detect directional predictability from economists’ oil price shocks to JUMPS. Bar graphs describe sample cross-quantilograms and red lines are the 95% bootstrap confidence intervals for 2000 bootstrap iterations. Note that $\alpha_{1}$, $\alpha_{2}$ correspond to the quantiles of JUMPS and oil shocks respectively

Fig. 6

Cross-quantilogram results. Sample cross-quantilograms for $\alpha_{2} = 0.9$ to detect directional predictability from economists’ oil price shocks to JUMPS. Bar graphs describe sample cross-quantilograms and red lines are the 95% bootstrap confidence intervals for 2000 bootstrap iterations. Note that $\alpha_{1}$, $\alpha_{2}$ correspond to the quantiles of JUMPS and oil shocks respectively

Fig. 7

Cross-quantilogram results. Sample cross-quantilograms for $\alpha_{2} = 0.1$ to detect directional predictability from financial market’s oil price shocks to JUMPS. Bar graphs describe sample cross-quantilograms and red lines are the 95% bootstrap confidence intervals for 2000 bootstrap iterations. Note that $\alpha_{1}$, $\alpha_{2}$ correspond to the quantiles of JUMPS and oil shocks respectively

Fig. 8

Cross-quantilogram results. Sample cross-quantilograms for $\alpha_{2} = 0.9$ to detect directional predictability from financial market’s oil price shocks to JUMPS. Bar graphs describe sample cross-quantilograms and red lines are the 95% bootstrap confidence intervals for 2000 bootstrap iterations. Note that $\alpha_{1}$, $\alpha_{2}$ correspond to the quantiles of JUMPS and oil shocks respectively

Fig. 9

Cross-quantilogram results. Sample cross-quantilograms for $\alpha_{2} = 0.1$ to detect directional predictability from policymakers’ oil price shocks to JUMPS. Bar graphs describe sample cross-quantilograms and red lines are the 95% bootstrap confidence intervals for 2000 bootstrap iterations. Note that $\alpha_{1}$, $\alpha_{2}$ correspond to the quantiles of JUMPS and oil shocks respectively

Fig. 10

Cross-quantilogram results. Sample cross-quantilograms for $\alpha_{2} = 0.9$ to detect directional predictability from policymakers’ oil price shocks to JUMPS. Bar graphs describe sample cross-quantilograms and red lines are the 95% bootstrap confidence intervals for 2000 bootstrap iterations. Note that $\alpha_{1}$, $\alpha_{2}$ correspond to the quantiles of JUMPS and oil shocks respectively

Table 3 Summary statistics

Table 4 Causality-in-quantiles test results for S&P500 volatility jumps due to positive and negative oil shocks

Table 5 Causality-in-quantiles test results for S&P500 volatility jumps due to financial market’s oil shocks at various forecast horizons