Gene

Quantum mechanics

Context (archaeology)

Forward volatility

Engineering

Volatility (finance)

Biology

Exploit

Stock market

Computer security

Robustness (evolution)

Petroleum engineering

Archaeology

Economics

Component (thermodynamics)

Biochemistry

Computer science

Geology

Jump diffusion

Paleontology

Thermodynamics

Physics

Geography

Realized variance

Econometrics

Environmental science

Stochastic volatility

Horse

Jump

Financial economics

Chemistry

Crude oil

Autoregressive conditional heteroskedasticity

Journal of Empirical Finance

Oil markets are subject to extreme shocks (e.g. Iraq’s invasion of Kuwait), causing the oil market price exhibits extreme movements, called jumps (or spikes). These jumps pose challenges on oil market volatility forecasting using conventional volatility dynamic models (e.g. GARCH model) This paper characterizes dynamics of jumps in oil market price using high frequency data from three perspectives: the probability (or intensity) of jump occurrence, the sign (e.g. positive or negative) of jumps, and the concurrence with stock market jumps. And then, the paper exploits predictive ability of these jump-related information for oil market volatility forecasting under the mixed data sampling (MIDAS) modeling framework. Our empirical results show that augmenting standard MIDAS model using the three jump-related information significantly improves the accuracy of oil market volatility forecasting. The jump intensity and negative jump size are particularly useful for predicting future oil volatility. These results are widely consistent across a variety of robustness tests. This work provides new insights on how to forecast oil market volatility in the presence of extreme shocks.

Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks