An oil futures volatility forecast perspective on the selection of high-frequency jump tests

Xiafei Li, Yin Liao, Xinjie Lu and Feng Ma

Energy Economics, 2022, vol. 116, issue C

Abstract: This paper examines the forecasting performances of high-frequency jump tests for oil futures volatility from a comprehensive perspective. It contributes to the literature by investigating which jump test is the best for oil futures volatility forecasting under different circumstances and whether the jump component extracted from multiple alternative tests is useful for further improving forecasting performance. Our results show that the jumps of the TOD test (Bollerslev et al., 2013) have satisfactory performance over the medium-term and especially the short-term forecasting horizons. Most importantly, the jump components from the intersection of multiple intraday tests further improve the forecasting performance. A variety of further discussions, including models controlling for stock market effects and considering periods of high (low) volatility and the COVID-19 pandemic period, confirm the conclusions. This paper attempts to shed light on oil futures volatility prediction from the perspective of jump test selection.

Keywords: Oil futures volatility; Jump test selection; Volatility forecasting; HAR-RV-type models; High-frequency data (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:116:y:2022:i:c:s014098832200487x

DOI: 10.1016/j.eneco.2022.106358

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