Which time-frequency domain dominates spillover in the Chinese energy stock market?
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DOI: 10.1016/j.irfa.2020.101641
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- Jingjian, Si & Xiangyun, Gao & Jinsheng, Zhou & Anjian, Wang & Xiaotian, Sun & Yiran, Zhao & Hongyu, Wei, 2023. "The impact of oil price shocks on energy stocks from the perspective of investor attention," Energy, Elsevier, vol. 278(PB).
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- Adedoyin Isola Lawal, 2023. "The Nexus between Economic Growth, Energy Consumption, Agricultural Output, and CO 2 in Africa: Evidence from Frequency Domain Estimates," Energies, MDPI, vol. 16(3), pages 1-27, January.
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- Jian, Zhihong & Lu, Haisong & Zhu, Zhican & Xu, Huiling, 2023. "Frequency heterogeneity of tail connectedness: Evidence from global stock markets," Economic Modelling, Elsevier, vol. 125(C).
- Xie, Qichang & Bai, Yu & Jia, Nanfei & Xu, Xin, 2024. "Do macroprudential policies reduce risk spillovers between energy markets?: Evidence from time-frequency domain and mixed-frequency methods," Energy Economics, Elsevier, vol. 134(C).
- Xi, Zenglei & Yu, Jinxiu & Sun, Qingru & Zhao, Wenqi & Wang, He & Zhang, Shuo, 2023. "Measuring the multi-scale price transmission effects from crude oil to energy stocks: A cascaded view," International Review of Financial Analysis, Elsevier, vol. 90(C).
- Xing, Xiaoyun & Xu, Zihan & Chen, Ying & Ouyang, WenPei & Deng, Jing & Pan, Huanxue, 2023. "The impact of the Russia–Ukraine conflict on the energy subsector stocks in China: A network-based approach," Finance Research Letters, Elsevier, vol. 53(C).
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More about this item
Keywords
Energy stocks; Spillover effect; Multiscale; Generalized variance decomposition; Wavelet method;All these keywords.
JEL classification:
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
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