Do Jumps Matter for Volatility Forecasting? Evidence from Energy Markets

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Forecasting the volatility of Bitcoin: The importance of jumps and structural breaks (2020, unknown): “Finally we examine two further models, namely the HAR-RJ model of Tauchen and Zhou (2011) and the HAR-ARJ of Prokopczuk et al (2016).”
Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks (2019, background): “Signed jump components In line with the works of Tauchen and Zhou (2011) and Prokopczuk et al. (2016), we further”
Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence (2018, unknown): “See, for example, Prokopczuk et al .”
Forecasting oil price realized volatility using information channels from other asset classes (2017, methodology): “The current consensus provides evidence that the HAR-RV model outperforms all other competing forecasting models (see, Haugom et al., 2014; Sévi, 2014; Prokopczuk et al., 2015).”
Forecasting the realized volatility of the oil futures market: A regime switching approach (2017, background): “For example, Degiannakis and George (2016) point out that studies, such as Haugom et al. (2014), Sévi (2014), Prokopczuk et al. (2016), and Wen et al. (2016), try to forecast oil price volatility using ultra-high frequency data.”