Does high-frequency crude oil futures data contain useful information for predicting volatility in the US stock market? New evidence

Jiqian Wang, Yisu Huang, Feng Ma and Julien Chevallier

Energy Economics, 2020, vol. 91, issue C

Abstract: This study examines whether high-frequency crude oil futures data contain useful information to forecast the realized volatility (RV) of the US stock market from both in- and out-of-sample perspectives. There are several significant findings. First, from the in-sample analysis, crude oil futures RV exhibits a significant positive impact on the future S&P 500 volatility. Second, the out-of-sample results reveal that the prediction models, including crude oil futures RV, outperform the related competing models, implying that crude oil RV is an important predictive factor for the US stock market. Third, we further find that the primary forecasting ability of crude oil RV is reflected in high-frequency information, negative crude oil RV, and high volatility level. Finally, the out-of-sample empirical results based on different forecasting windows, alternative forecast evaluation approaches, subsample analysis, different prediction models, alternative MIDAS lags, and controlling the leverage effect are robust to our conclusions.

Keywords: High-frequency data; Crude oil futures; Stock market; Realized volatility; Forecasting (search for similar items in EconPapers)
JEL-codes: C53 G12 Q47 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:91:y:2020:i:c:s0140988320302371

DOI: 10.1016/j.eneco.2020.104897

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