2022
DOI: 10.1016/j.resourpol.2021.102453
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Forecasting China's crude oil futures volatility: New evidence from the MIDAS-RV model and COVID-19 pandemic

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Cited by 20 publications
(4 citation statements)
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“…Input parameters of the hybrid MLR-FFANN model 22)- (24). The MAE, MAPE, and RMSE evaluation criteria results from the training and testing data of different FFANN structures for all working cases of the AGDE algorithm are given in Table 5.…”
Section: Resultsmentioning
confidence: 99%
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“…Input parameters of the hybrid MLR-FFANN model 22)- (24). The MAE, MAPE, and RMSE evaluation criteria results from the training and testing data of different FFANN structures for all working cases of the AGDE algorithm are given in Table 5.…”
Section: Resultsmentioning
confidence: 99%
“…Furthermore, it has been reported that the leverage effect is the strongest predictor of the COVID-19 pandemic based on the samples collected during the pandemic. 24 In another study, Wu et al analyzed the US oil markets based on social media information using convolutional NN (CNN) during the COVID-19 process. The results obtained from the experimental studies showed that although social media information contributes to the estimation of oil price, production and consumption, the oil inventory does not affect the estimation accuracy.…”
Section: Related Workmentioning
confidence: 99%
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“…In recent studies, Chen, Z. et al [31] used the MIDAS modeling framework, which is a common mixed data sampling approach, to investigate how leverage and jumps affect the ability to forecast the realized volatility (RV) of China's crude oil futures. This study's findings can be utilized to better predict the volatility of China's crude oil futures, reduce investment risks, and provide higher profits.…”
Section: Introductionmentioning
confidence: 99%