IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v140y2024ics0140988324007278.html
   My bibliography  Save this article

Scrutinizing multi-scale and multi-quantile interactions in commodity markets: A petrochemical industrial chain perspective

Author

Listed:
  • Yang, Jie
  • Feng, Yun
  • Yang, Hao

Abstract

From the perspective of the petrochemical industrial chain, this paper examines the interactions among five China's petrochemical commodity futures using three innovative methods - wavelet local multiple correlation, frequency connectedness framework, and quantile connectedness framework. The results show China's petrochemical markets exhibit a high degree of market integration at different time scales but decouple from international crude oil markets in the short term. The price dynamics of polypropylene (PP) and linear low-density polyethylene (LL) behave as the dominant factors to impact the price fluctuations of other commodities. The total information spillover level showcases a rapidly decreasing trend with the time scale increasing but a U-shaped curve across various quantiles and reaches the minimum at the 50th percentile. We further identified the net information transmitters and recipients in the industrial chain system and also explored the spillover shocks of two globally traded crude oil benchmarks, i.e., Brent and WTI, at different time scales and under different market conditions. They virtually always serve as net risk transmitters to China's domestic markets, but under extremely bullish market conditions, they are net influenced by the sharply upward trends of China's markets.

Suggested Citation

  • Yang, Jie & Feng, Yun & Yang, Hao, 2024. "Scrutinizing multi-scale and multi-quantile interactions in commodity markets: A petrochemical industrial chain perspective," Energy Economics, Elsevier, vol. 140(C).
  • Handle: RePEc:eee:eneeco:v:140:y:2024:i:c:s0140988324007278
    DOI: 10.1016/j.eneco.2024.108019
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988324007278
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2024.108019?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    2. Liu, Min & Lee, Chien-Chiang, 2021. "Capturing the dynamics of the China crude oil futures: Markov switching, co-movement, and volatility forecasting," Energy Economics, Elsevier, vol. 103(C).
    3. Zhang-Hangjian Chen & Xiang Gao & Apicha Insuwan, 2023. "Dynamic information spillover between Chinese carbon and stock markets under extreme weather shocks," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    4. Duan, Kun & Ren, Xiaohang & Wen, Fenghua & Chen, Jinyu, 2023. "Evolution of the information transmission between Chinese and international oil markets: A quantile-based framework," Journal of Commodity Markets, Elsevier, vol. 29(C).
    5. Adams, Zeno & Glück, Thorsten, 2015. "Financialization in commodity markets: A passing trend or the new normal?," Journal of Banking & Finance, Elsevier, vol. 60(C), pages 93-111.
    6. Wei, Yu & Wang, Yizhi & Vigne, Samuel A. & Ma, Zhenyu, 2023. "Alarming contagion effects: The dangerous ripple effect of extreme price spillovers across crude oil, carbon emission allowance, and agriculture futures markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    7. Zhao, Mingguo & Park, Hail, 2024. "Quantile time-frequency spillovers among green bonds, cryptocurrencies, and conventional financial markets," International Review of Financial Analysis, Elsevier, vol. 93(C).
    8. Dai, Zhifeng & Zhu, Junxin & Zhang, Xinhua, 2022. "Time-frequency connectedness and cross-quantile dependence between crude oil, Chinese commodity market, stock market and investor sentiment," Energy Economics, Elsevier, vol. 114(C).
    9. Cui, Jinxin & Maghyereh, Aktham, 2024. "Unveiling interconnectedness: Exploring higher-order moments among energy, precious metals, industrial metals, and agricultural commodities in the context of geopolitical risks and systemic stress," Journal of Commodity Markets, Elsevier, vol. 33(C).
    10. Ouyang, Ruolan & Zhuang, Chengkai & Wang, Tingting & Zhang, Xuan, 2022. "Network analysis of risk transmission among energy futures: An industrial chain perspective," Energy Economics, Elsevier, vol. 107(C).
    11. Ouyang, Ruolan & Zhang, Xuan, 2020. "Financialization of agricultural commodities: Evidence from China," Economic Modelling, Elsevier, vol. 85(C), pages 381-389.
    12. Wang, Suhui, 2023. "Tail dependence, dynamic linkages, and extreme spillover between the stock and China's commodity markets," Journal of Commodity Markets, Elsevier, vol. 29(C).
    13. Zeng, Hongjun & Abedin, Mohammad Zoynul & Zhou, Xiangjing & Lu, Ran, 2024. "Measuring the extreme linkages and time-frequency co-movements among artificial intelligence and clean energy indices," International Review of Financial Analysis, Elsevier, vol. 92(C).
    14. Yang, Yuying & Ma, Yan-Ran & Hu, Min & Zhang, Dayong & Ji, Qiang, 2021. "Extreme risk spillover between chinese and global crude oil futures," Finance Research Letters, Elsevier, vol. 40(C).
    15. Bouri, Elie & Nekhili, Ramzi & Todorova, Neda, 2023. "Dynamic co-movement in major commodity markets during crisis periods: A wavelet local multiple correlation analysis," Finance Research Letters, Elsevier, vol. 55(PB).
    16. Shi, Xunpeng & Sun, Sizhong, 2017. "Energy price, regulatory price distortion and economic growth: A case study of China," Energy Economics, Elsevier, vol. 63(C), pages 261-271.
    17. Cagli, Efe Caglar, 2023. "The volatility spillover between battery metals and future mobility stocks: Evidence from the time-varying frequency connectedness approach," Resources Policy, Elsevier, vol. 86(PA).
    18. Zhang, Dayong & Shi, Min & Shi, Xunpeng, 2018. "Oil indexation, market fundamentals, and natural gas prices: An investigation of the Asian premium in natural gas trade," Energy Economics, Elsevier, vol. 69(C), pages 33-41.
    19. Sang Hoon Kang & Seong‐Min Yoon, 2020. "Dynamic correlation and volatility spillovers across Chinese stock and commodity futures markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 25(2), pages 261-273, April.
    20. Jozef Baruník & Tomáš Křehlík, 2018. "Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk," Journal of Financial Econometrics, Oxford University Press, vol. 16(2), pages 271-296.
    21. Li, Yanshuang & Shi, Yujie & Shi, Yongdong & Xiong, Xiong & Yi, Shangkun, 2024. "Time-frequency extreme risk spillovers between COVID-19 news-based panic sentiment and stock market volatility in the multi-layer network: Evidence from the RCEP countries," International Review of Financial Analysis, Elsevier, vol. 94(C).
    22. Zhi Da & Ke Tang & Yubo Tao & Liyan Yang, 2024. "Financialization and Commodity Markets Serial Dependence," Management Science, INFORMS, vol. 70(4), pages 2122-2143, April.
    23. 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).
    24. Shah, Muhammad Ibrahim & Foglia, Matteo & Shahzad, Umer & Fareed, Zeeshan, 2022. "Green innovation, resource price and carbon emissions during the COVID-19 times: New findings from wavelet local multiple correlation analysis," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    25. Tomohiro Ando & Matthew Greenwood-Nimmo & Yongcheol Shin, 2022. "Quantile Connectedness: Modeling Tail Behavior in the Topology of Financial Networks," Management Science, INFORMS, vol. 68(4), pages 2401-2431, April.
    26. AlKathiri, Nader & Al-Rashed, Yazeed & Doshi, Tilak K. & Murphy, Frederic H., 2017. "“Asian premium” or “North Atlantic discount”: Does geographical diversification in oil trade always impose costs?," Energy Economics, Elsevier, vol. 66(C), pages 411-420.
    27. Wei, Yu & Zhang, Yaojie & Wang, Yudong, 2022. "Information connectedness of international crude oil futures: Evidence from SC, WTI, and Brent," International Review of Financial Analysis, Elsevier, vol. 81(C).
    28. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    29. López Cabrera, Brenda & Schulz, Franziska, 2016. "Volatility linkages between energy and agricultural commodity prices," Energy Economics, Elsevier, vol. 54(C), pages 190-203.
    30. Chen, Hao & Sun, Zesheng, 2021. "International crude oil price, regulation and asymmetric response of China's gasoline price," Energy Economics, Elsevier, vol. 94(C).
    31. Qiang Ji & Dayong Zhang & Yuqian Zhao, 2022. "Intra-day co-movements of crude oil futures: China and the international benchmarks," Annals of Operations Research, Springer, vol. 313(1), pages 77-103, June.
    32. Asadi, Mehrad & Roudari, Soheil & Tiwari, Aviral Kumar & Roubaud, David, 2023. "Scrutinizing commodity markets by quantile spillovers: A case study of the Australian economy," Energy Economics, Elsevier, vol. 118(C).
    33. Gao, Yang & Zhou, Yueyi & Zhao, Longfeng, 2024. "Quantile interdependence and network connectedness between China's green financial and energy markets," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 1148-1177.
    34. Adams, Zeno & Glueck, Thorsten, 2014. "Financialization in Commodity Markets: A Passing Trend or the New Normal?," Working Papers on Finance 1413, University of St. Gallen, School of Finance, revised Aug 2015.
    35. Ren, Yinghua & Tan, Anqi & Zhu, Huiming & Zhao, Wanru, 2022. "Does economic policy uncertainty drive nonlinear risk spillover in the commodity futures market?," International Review of Financial Analysis, Elsevier, vol. 81(C).
    36. Gong, Xu & Xu, Jun, 2022. "Geopolitical risk and dynamic connectedness between commodity markets," Energy Economics, Elsevier, vol. 110(C).
    37. Polanco Martínez, Josué M. & Abadie, Luis M. & Fernández-Macho, J., 2018. "A multi-resolution and multivariate analysis of the dynamic relationships between crude oil and petroleum-product prices," Applied Energy, Elsevier, vol. 228(C), pages 1550-1560.
    38. Guo, Yangli & Li, Pan & Wu, Hanlin, 2023. "Jumps in the Chinese crude oil futures volatility forecasting: New evidence," Energy Economics, Elsevier, vol. 126(C).
    39. Sun, Guanglin & Li, Jianfeng & Shang, Zezhong, 2022. "Return and volatility linkages between international energy markets and Chinese commodity market," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Jie & Feng, Yun & Yang, Hao, 2024. "The spillover and comovement of downside and upside tail risks among crude oil futures markets," International Review of Financial Analysis, Elsevier, vol. 96(PA).
    2. Yang, Jie & Feng, Yun & Yang, Hao, 2025. "Multiscale dynamic interdependency between China’s crude oil futures and petrochemical-related commodity futures: An integrated perspective from the industry chain system," The North American Journal of Economics and Finance, Elsevier, vol. 75(PA).
    3. Yang, Jie & Feng, Yun & Yang, Hao, 2024. "Commodity connectedness of the petrochemical industrial chain: A novel perspective of “good” and “bad” volatility surprises," Finance Research Letters, Elsevier, vol. 67(PB).
    4. Jun Long & Xianghui Yuan & Liwei Jin & Chencheng Zhao, 2024. "Connectedness and risk spillover in China's commodity futures sectors," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(5), pages 784-802, May.
    5. Mensi, Walid & Ahmadian-Yazdi, Farzaneh & Al-Kharusi, Sami & Roudari, Soheil & Kang, Sang Hoon, 2024. "Extreme Connectedness Across Chinese Stock and Commodity Futures Markets," Research in International Business and Finance, Elsevier, vol. 70(PA).
    6. Kočenda, Evžen & Moravcová, Michala, 2024. "Frequency volatility connectedness and portfolio hedging of U.S. energy commodities," Research in International Business and Finance, Elsevier, vol. 69(C).
    7. Zhu, Yanli & Yang, Xian & Zhang, Chuanhai & Liu, Sihan & Li, Jiayi, 2024. "Asymmetric multi-scale systemic risk spillovers across international commodity futures markets: The role of infectious disease uncertainty," Journal of Commodity Markets, Elsevier, vol. 36(C).
    8. Yuan, Xianghui & Long, Jun & Li, Xiang & Zhao, Chencheng, 2025. "Asymmetric connectedness in the Chinese stock sectors: Overnight and daytime return spillovers," Pacific-Basin Finance Journal, Elsevier, vol. 89(C).
    9. Su, Tong & Lin, Boqiang, 2024. "Reassessing the information transmission and pricing influence of Shanghai crude oil futures: A time-varying perspective," Energy Economics, Elsevier, vol. 140(C).
    10. Biswas, Priti & Jain, Prachi & Maitra, Debasish, 2024. "Are shocks in the stock markets driven by commodity markets? Evidence from Russia-Ukraine war," Journal of Commodity Markets, Elsevier, vol. 34(C).
    11. Jiang, Wei & Chen, Yunfei, 2024. "Impact of Russia-Ukraine conflict on the time-frequency and quantile connectedness between energy, metal and agricultural markets," Resources Policy, Elsevier, vol. 88(C).
    12. Shi, Chunpei & Wei, Yu & Li, Xiafei & Liu, Yuntong, 2023. "Combination forecasts of China's oil futures returns based on multiple uncertainties and their connectedness with oil," Energy Economics, Elsevier, vol. 126(C).
    13. Zhu, Huiming & Xia, Xiling & Hau, Liya & Zeng, Tian & Deng, Xi, 2024. "Time-frequency higher-order moment Co-movement and connectedness between Chinese stock and commodity markets," International Review of Economics & Finance, Elsevier, vol. 96(PA).
    14. Yan, Wan-Lin & Cheung, Adrian (Wai Kong), 2024. "Connectedness among Chinese climate policy uncertainty, exchange rate, Chinese and international crude oil markets: Insights from time and frequency domain analyses of high order moments," The North American Journal of Economics and Finance, Elsevier, vol. 73(C).
    15. Ben Amar, Amine & Goutte, Stéphane & Isleimeyyeh, Mohammad, 2022. "Asymmetric cyclical connectedness on the commodity markets: Further insights from bull and bear markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 386-400.
    16. Wei, Yu & Wang, Yizhi & Vigne, Samuel A. & Ma, Zhenyu, 2023. "Alarming contagion effects: The dangerous ripple effect of extreme price spillovers across crude oil, carbon emission allowance, and agriculture futures markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    17. Su, Xianfang & Zhao, Yachao, 2023. "What has the strongest connectedness with clean energy? Technology, substitutes, or raw materials," Energy Economics, Elsevier, vol. 128(C).
    18. Wang, Jue & Zhou, Yuqin & Wu, Shan, 2025. "Quantile time-frequency connectedness and portfolio diversification: A study of clean energy and metal markets," Renewable Energy, Elsevier, vol. 238(C).
    19. Greenwood-Nimmo, Matthew & Kočenda, Evžen & Nguyen, Viet Hoang, 2024. "Detecting statistically significant changes in connectedness: A bootstrap-based technique," Economic Modelling, Elsevier, vol. 140(C).
    20. Cui, Jinxin & Maghyereh, Aktham, 2023. "Higher-order moment risk connectedness and optimal investment strategies between international oil and commodity futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict," International Review of Financial Analysis, Elsevier, vol. 86(C).

    More about this item

    Keywords

    WLMC; Frequency connectedness; Quantile connectedness; Commodity futures; Petrochemical industrial chain;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:140:y:2024:i:c:s0140988324007278. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.