Coherence, extreme risk spillovers, and dynamic linkages between oil and China?s commodity futures markets

被引:41
|
作者
Cui, Jinxin [1 ,2 ]
Goh, Mark [2 ]
Zou, Huiwen [1 ]
机构
[1] Fuzhou Univ, Sch Econ & Management, Inst Investment & Risk Management, Fuzhou 350116, Peoples R China
[2] Natl Univ Singapore, NUS Business Sch & Logist Inst Asia Pacific, Singapore, Singapore
基金
中国国家自然科学基金;
关键词
Wavelet coherence; Quantile connectedness; DECO-FIAPARCH; China?s commodity futures; CRUDE-OIL; STOCK MARKETS; WAVELET COHERENCE; PRECIOUS METALS; PRICE SHOCKS; CONDITIONAL HETEROSCEDASTICITY; SECTORS EVIDENCE; CO-MOVEMENT; TIME-SERIES; CRISIS;
D O I
10.1016/j.energy.2021.120190
中图分类号
O414.1 [热力学];
学科分类号
摘要
This paper investigates the time-frequency dependence, extreme risk spillovers, and dynamic linkages between oil and China's commodity futures markets, using wavelet coherence, quantile connectedness approach, and the DECO-FIAPARCH (1,d,1) model. The results suggest that the oil market exhibits higher coherence with the copper, natural rubber, and fuel oil futures but low coherence with corn, soybean, soybean meal, and white sugar futures on a long-term scale. The crude oil market leads most of China's commodity futures. The results of the DECO model point to the time-varying and low average equi-correlations between oil and China's commodity futures. The total risk connectedness at the extreme lower quantile level (0.01) is higher than the conditional mean and conditional median level. WTI oil, Brent oil, soybean oil, and copper futures are the main spillover net-transmitters whereas the white sugar, soybean, soybean meal, cotton, corn, aluminum, natural rubber, and fuel oil futures are risk spillover net-recipients. The dynamic extreme negative risk spillovers are highly volatile and vulnerable to major international events such as the GFC, oil price plunge, and the COVID-19 pandemic. Finally, Brent oil offers better portfolio diversification benefits than WTI oil and the optimal-weighted portfolio illustrates the highest risk and downside risk reduction effectiveness. (c) 2021 Elsevier Ltd. All rights reserved.
引用
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页数:27
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