Forecasting the volatility of crude oil futures using HAR-type models with structural breaks

By Fenghua Wen, Xu Gong, Shenghua Cai. 2016

259 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/71c16e8f56b84fc3e33a10dac77102365adc4ee8

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Citations in the corpus

Forecasting realized volatility of agricultural commodity futures with infinite Hidden Markov HAR models (2019, unknown): “…commodities to forecast volatility is much less focused on than on other commodities such as crude oil (Souˇcek & Todorova, 2013, S´evi, 2014, Wen et al., 2016, Degiannakis & Filis, 2017), precious metals (Caporin et al., 2015, Ly´ocsa & Moln´ar, 2016) or non-ferrous metals (Todorova et al.,…”
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.”
The pass-through effects of oil price shocks on China's inflation: A time-varying analysis (2020, background): “Therefore, China’s sustainable economic development is highly dependent on oil (Wen et al., 2016; Gong and Lin, 2017; Zhang et al., 2017; Gong and Lin, 2018a).”
Asymmetric Impact of Oil Price Shock on Stock Market in China: A Combination Analysis Based on SVAR Model and NARDL Model (2017, background): “Furthermore, risk prediction of the crude oil market also has aroused great concern among scholars (Kang, Kang, and Yoon 2009; Wen, Gong, and Cai 2016).”
Forecasting the volatility of Bitcoin: The importance of jumps and structural breaks (2020, unknown): “5 Liu et al (2015) show that it is difficult to beat the standard 5-minute realized variance when forecasting volatility and the use of the 5-minute frequency is consistent with other studies such as Sévi (2014); Wen (2016), Behrendt and Schmidt (2018).”