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Correlation between Shanghai crude oil futures, stock, foreign exchange, and gold markets: a GARCH-vine-copula method

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  • Chaohua He
  • Guangchen Li
  • Hai Fan
  • Weixian Wei

Abstract

The relationship between markets has always been a topic of heated debate among scholars from various countries. One of the most important concerns is the need to model the relationships between the crude oil market and other markets. Based on daily return observations from 2018 to 2019, we apply a GARCH-vine-copula approach to probe the linkage between Shanghai crude oil futures, stock, foreign exchange, and gold markets. We find that obvious tail dependencies do exist between these markets. And the crude oil futures market occupies a dominant position. Moreover, when the Shanghai crude oil futures market is taken as the known condition, the links between different markets reduce to some extent. Finally, value at risk results denote that the risk of the Shanghai crude oil futures market is relatively high, but portfolio investment can effectively reduce the risk. Moreover, the model fitting results at different confidence levels have passed the Kupiec backtest, indicating that the model in this paper fits the relationship between these markets well.

Suggested Citation

  • Chaohua He & Guangchen Li & Hai Fan & Weixian Wei, 2021. "Correlation between Shanghai crude oil futures, stock, foreign exchange, and gold markets: a GARCH-vine-copula method," Applied Economics, Taylor & Francis Journals, vol. 53(11), pages 1249-1263, March.
    • Handle: RePEc:taf:applec:v:53:y:2021:i:11:p:1249-1263
    DOI: 10.1080/00036846.2020.1828566
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    Cited by:

    1. Ben Salem, Leila & Zayati, Montassar & Nouira, Ridha & Rault, Christophe, 2024. "Volatility spillover between oil prices and main exchange rates: Evidence from a DCC-GARCH-connectedness approach," Resources Policy, Elsevier, vol. 91(C).
    2. Naqvi, Bushra & Mirza, Nawazish & Umar, Muhammad & Rizvi, Syed Kumail Abbas, 2023. "Shanghai crude oil futures: Returns Independence, volatility asymmetry, and hedging potential," Energy Economics, Elsevier, vol. 128(C).
    3. Li, Guangchen & Shen, Z.Y. & Song, Malin & Wei, Weixian, 2024. "Exploring the interconnectedness of China's new energy and stock markets: A study on volatility spillovers and dynamic correlations," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 471-484.
    4. Yan-Hong Yang & Ying-Lin Liu & Ying-Hui Shao, 2023. "Visibility graph analysis of crude oil futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict," Papers 2310.18903, arXiv.org, revised Jun 2024.
    5. Pier Francesco Procacci & Tomaso Aste, 2022. "Portfolio optimization with sparse multivariate modeling," Journal of Asset Management, Palgrave Macmillan, vol. 23(6), pages 445-465, October.
    6. Liu, Zixin & Hu, Jun & Zhang, Shuguang & He, Zhipeng, 2024. "Risk spillovers among oil, gold, stock, and foreign exchange markets: Evidence from G20 economies," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    7. Li, Jiang-Cheng & Xu, Ming-Zhe & Han, Xu & Tao, Chen, 2022. "Dynamic risk resonance between crude oil and stock market by econophysics and machine learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    8. Luo, Changqing & Qu, Yi & Su, Yaya & Dong, Liang, 2024. "Risk spillover from international crude oil markets to China’s financial markets: Evidence from extreme events and U.S. monetary policy," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).

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