Shanghai crude oil futures: Returns Independence, volatility asymmetry, and hedging potential

被引:10
作者
Naqvi, Bushra [1 ]
Mirza, Nawazish [2 ]
Umar, Muhammad [3 ]
Rizvi, Syed Kumail Abbas [1 ]
机构
[1] Lahore Univ Management Sci, Suleman Dawood Sch Business, Lahore, Pakistan
[2] Excelia Business Sch, La Rochelle, France
[3] Lebanese Amer Univ, Adnan Kassar Sch Business, Dept Finance & Accounting, Beirut, Lebanon
关键词
SCOF WTI Brent Volatility dynamics Crude oil futures; STOCK; PRICE; SPILLOVERS; MARKET; CHINA;
D O I
10.1016/j.eneco.2023.107110
中图分类号
F [经济];
学科分类号
02 ;
摘要
Amidst the rise of Shanghai crude oil futures (SCOF) as a preeminent contender in the global oil arena, this study analyzes its returns and volatility structures in compelling contrast to West Texas Intermediate (WTI) and Brent oil futures (BRENT). A comprehensive examination is conducted using various GARCH models and News impact curves, with the analysis based on daily data spanning from April 2021 to March 2023. The results reveal distinct responses of SCOF when contrasted with WTI and Brent. Firstly, the study finds that SCOF returns exhibit a level of independence from global market movements. Secondly, the assessment of potential asymmetries in the volatility structures displays notable differences among the three markets. Specifically, WTI demonstrates the highest asymmetry, while SCOF exhibits lower asymmetry. These findings imply that SCOF returns exhibit stability and resilience and hold the potential to serve as a formidable hedge against adverse shocks. As investors and policymakers navigate the complex terrain of the global oil market, these insights underscore the strategic advantages and opportunities that SCOF may offer, both in individual investment decisions and broader risk management strategies.
引用
收藏
页数:11
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共 48 条
[41]   The Impacts of the Infectious Disease Epidemic on the Permanent Volatility of Precious Metal and Crude Oil Futures Markets: A Long-Term Perspective [J].
Shang, Yue ;
Chen, Xiaodan ;
Zhang, Yifeng ;
Wei, Yu .
DISCRETE DYNAMICS IN NATURE AND SOCIETY, 2021, 2021
[42]   Investing in cocoa-gold sector and the crude oil price-exchange rate uncertainty in Ghana: Volatility transmission and hedging approach [J].
Damba, Osman Tahidu ;
Bilgic, Abdulbaki ;
Amikuzuno, Joseph ;
Ibrahim, Muazu .
AFRICAN REVIEW OF ECONOMICS AND FINANCE-AREF, 2021, 13 (01) :193-213
[43]   A combined model using secondary decomposition for crude oil futures price and volatility forecasting: Analysis based on comparison and ablation experiments [J].
Gong, Hao ;
Xing, Haiyang ;
Yu, Yuanyuan ;
Liang, Yanhui .
EXPERT SYSTEMS WITH APPLICATIONS, 2024, 252
[44]   A study of Shanghai fuel oil futures price volatility based on high frequency data: Long-range dependence, modeling and forecasting [J].
Liu, Li ;
Wan, Jieqiu .
ECONOMIC MODELLING, 2012, 29 (06) :2245-2253
[45]   Does high-frequency crude oil futures data contain useful information for predicting volatility in the US stock market? New evidence [J].
Wang, Jiqian ;
Huang, Yisu ;
Ma, Feng ;
Chevallier, Julien .
ENERGY ECONOMICS, 2020, 91 (91)
[46]   The role of global economic policy uncertainty in predicting crude oil futures volatility: Evidence from a two-factor GARCH-MIDAS model [J].
Dai, Peng-Fei ;
Xiong, Xiong ;
Zhang, Jin ;
Zhou, Wei-Xing .
RESOURCES POLICY, 2022, 78
[47]   Contemporaneous dependence between euro, crude oil, and gold returns and their respective implied volatility changes. Evidence from the local Gaussian correlation approach [J].
Fousekis, Panos .
STUDIES IN ECONOMICS AND FINANCE, 2023, 40 (05) :795-813
[48]   Returns and volatility linkages between international crude oil price, metal and other stock indices in India: Evidence from VAR-DCC-GARCH models [J].
Singhal, Shelly ;
Ghosh, Sajal .
RESOURCES POLICY, 2016, 50 :276-288