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 espe-cially 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 predic-tion from the perspective of jump test selection.
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Cent S Univ, Sch Business, Changsha 410083, Hunan, Peoples R China
Xiamen Univ, China Inst Studies Energy Policy, Collaborat Innovat Ctr Energy Econ & Energy Polic, Xiamen 361005, Peoples R ChinaCent S Univ, Sch Business, Changsha 410083, Hunan, Peoples R China
Gong, Xu
Wen, Fenghua
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Cent S Univ, Sch Business, Changsha 410083, Hunan, Peoples R China
Univ Windsor, Supply Chain & Logist Optimizat Res Ctr, Fac Engn, Windsor, ON, CanadaCent S Univ, Sch Business, Changsha 410083, Hunan, Peoples R China
Wen, Fenghua
Xia, X. H.
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Renmin Univ China, Sch Econ, Beijing 100872, Peoples R China
Renmin Univ China, Inst Chinas Econ Reform & Dev, Beijing 100872, Peoples R ChinaCent S Univ, Sch Business, Changsha 410083, Hunan, Peoples R China
Xia, X. H.
Huang, Jianbai
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Cent S Univ, Sch Business, Changsha 410083, Hunan, Peoples R ChinaCent S Univ, Sch Business, Changsha 410083, Hunan, Peoples R China
Huang, Jianbai
Pan, Bin
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Wenzhou Univ, Inst Finance, Wenzhou 325035, Peoples R ChinaCent S Univ, Sch Business, Changsha 410083, Hunan, Peoples R China
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Univ Oxford, Oxford Man Inst Quantitat Finance, Oxford, England
Univ Carlos III Madrid, Signal Proc & Learning Grp, Madrid, SpainUniv Oxford, Oxford Man Inst Quantitat Finance, Oxford, England
Moreno-Pino, Fernando
Zohren, Stefan
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Univ Oxford, Oxford Man Inst Quantitat Finance, Oxford, England
Univ Oxford, Machine Learning Res Grp, Oxford, EnglandUniv Oxford, Oxford Man Inst Quantitat Finance, Oxford, England