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How do China's oil markets affect other commodity markets both domestically and internationally?

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  • Ji, Qiang
  • Fan, Ying

Abstract

This study investigates the contemporaneous causality between China's oil markets with other commodity markets both domestically and internationally using an error correction model combined with a directed acyclic graph technique. The results indicate that China's oil markets are cointegrated with other commodity markets both domestically and internationally. The impact of China's fuel oil futures market on other domestic commodity markets is great when oil prices are high, but the influence is comparatively weak when oil prices are lower. However, due to the lack of futures market, China's crude oil market has little influence on other commodity markets at any time.

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  • Ji, Qiang & Fan, Ying, 2016. "How do China's oil markets affect other commodity markets both domestically and internationally?," Finance Research Letters, Elsevier, vol. 19(C), pages 247-254.
    • Handle: RePEc:eee:finlet:v:19:y:2016:i:c:p:247-254
    DOI: 10.1016/j.frl.2016.08.009
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    Cited by:

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    3. Salisu, Afees A. & Isah, Kazeem O., 2018. "Predicting US inflation: Evidence from a new approach," Economic Modelling, Elsevier, vol. 71(C), pages 134-158.
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    6. Meng, Juan & Nie, He & Mo, Bin & Jiang, Yonghong, 2020. "Risk spillover effects from global crude oil market to China’s commodity sectors," Energy, Elsevier, vol. 202(C).
    7. Luo, Jiawen & Ji, Qiang, 2018. "High-frequency volatility connectedness between the US crude oil market and China's agricultural commodity markets," Energy Economics, Elsevier, vol. 76(C), pages 424-438.
    8. Ji, Qiang & Bouri, Elie & Roubaud, David & Shahzad, Syed Jawad Hussain, 2018. "Risk spillover between energy and agricultural commodity markets: A dependence-switching CoVaR-copula model," Energy Economics, Elsevier, vol. 75(C), pages 14-27.
    9. Ye, Wuyi & Guo, Ranran & Deschamps, Bruno & Jiang, Ying & Liu, Xiaoquan, 2021. "Macroeconomic forecasts and commodity futures volatility," Economic Modelling, Elsevier, vol. 94(C), pages 981-994.
    10. Xiangcai Meng, 2018. "Does Agricultural Commodity Price Co-move with Oil Price in the Time-Frequency Space? Evidence from the Republic of Korea," International Journal of Energy Economics and Policy, Econjournals, vol. 8(4), pages 125-133.
    11. Chen, Hao & Xu, Chao & Peng, Yun, 2022. "Time-frequency connectedness between energy and nonenergy commodity markets during COVID-19: Evidence from China," Resources Policy, Elsevier, vol. 78(C).
    12. Ping, Li & Ziyi, Zhang & Tianna, Yang & Qingchao, Zeng, 2018. "The relationship among China’s fuel oil spot, futures and stock markets," Finance Research Letters, Elsevier, vol. 24(C), pages 151-162.
    13. Wood, Dallas & Larson, Justin & Jones, Jason & Galperin, Diana & Shelby, Michael & Gonzalez, Manuel, 2022. "World oil price impacts on country-specific fuel markets: Evidence of a muted global rebound effect," Energy Economics, Elsevier, vol. 111(C).
    14. Hou, Yang & Li, Steven & Wen, Fenghua, 2019. "Time-varying volatility spillover between Chinese fuel oil and stock index futures markets based on a DCC-GARCH model with a semi-nonparametric approach," Energy Economics, Elsevier, vol. 83(C), pages 119-143.
    15. Chen, Peng & He, Limin & Yang, Xuan, 2021. "On interdependence structure of China's commodity market," Resources Policy, Elsevier, vol. 74(C).
    16. Uddin, Gazi Salah & Luo, Tianqi & Yahya, Muhammad & Jayasekera, Ranadeva & Rahman, Md Lutfur & Okhrin, Yarema, 2023. "Risk network of global energy markets," Energy Economics, Elsevier, vol. 125(C).
    17. Zhang, Qi & Di, Peng & Farnoosh, Arash, 2021. "Study on the impacts of Shanghai crude oil futures on global oil market and oil industry based on VECM and DAG models," Energy, Elsevier, vol. 223(C).
    18. Babalos, Vassilios & Balcilar, Mehmet, 2017. "Does institutional trading drive commodities prices away from their fundamentals: Evidence from a nonparametric causality-in-quantiles test," Finance Research Letters, Elsevier, vol. 21(C), pages 126-131.
    19. Zhang, Dayong & Ji, Qiang & Kutan, Ali M., 2019. "Dynamic transmission mechanisms in global crude oil prices: Estimation and implications," Energy, Elsevier, vol. 175(C), pages 1181-1193.
    20. Wu, Fei & Zhao, Wan-Li & Ji, Qiang & Zhang, Dayong, 2020. "Dependency, centrality and dynamic networks for international commodity futures prices," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 118-132.
    21. Zhang, Dayong & Broadstock, David C., 2020. "Global financial crisis and rising connectedness in the international commodity markets," International Review of Financial Analysis, Elsevier, vol. 68(C).
    22. Su, Zhi & Liu, Peng & Fang, Tong, 2022. "Uncertainty matters in US financial information spillovers: Evidence from a directed acyclic graph approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 229-242.
    23. Gu, Fu & Wang, Jiqiang & Guo, Jianfeng & Fan, Ying, 2020. "Dynamic linkages between international oil price, plastic stock index and recycle plastic markets in China," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 167-179.
    24. Salisu, Afees A. & Isah, Kazeem O., 2018. "Predicting US inflation: Evidence from a new approach," Economic Modelling, Elsevier, vol. 71(C), pages 134-158.

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    More about this item

    Keywords

    Information transmission; Directed acyclic graph method; Commodity market;
    All these keywords.

    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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