Forecasting China's crude oil futures volatility: How to dig out the information of other energy futures volatilities?
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DOI: 10.1016/j.resourpol.2022.102852
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Citations
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Cited by:
- Guo, Lili & Huang, Xinya & Li, Yanjiao & Li, Houjian, 2023. "Forecasting crude oil futures price using machine learning methods: Evidence from China," Energy Economics, Elsevier, vol. 127(PA).
- Liu, Yanqiong & Lu, Jinjin & Shi, Fengyuan, 2023. "Spillover relationship between different oil shocks and high- and low-carbon assets: An analysis based on time-frequency spillover effects," Finance Research Letters, Elsevier, vol. 58(PC).
- Wang, Jia & Wang, Xinyi & Wang, Xu, 2024. "International oil shocks and the volatility forecasting of Chinese stock market based on machine learning combination models," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
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More about this item
Keywords
Crude oil; Energy futures; Volatility forecasting; Forecast combinations; Shrinkage methods;All these keywords.
JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
- Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
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