IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v132y2024ics0140988324001774.html

Extreme time-varying spillovers between high carbon emission stocks, green bond and crude oil: Comment

Author

Listed:
  • Bonaccolto, Giovanni
  • Caporin, Massimiliano
  • Iacopini, Matteo

Abstract

In this article, we provide a comment on the work of Dai et al. (2023), who introduced the Time-Varying Parameters Quantile Vector Auto Regressive model (TVP-QVAR) to analyze the spillovers between high carbon emission stocks, green bonds, and crude oil. We argue that some peculiar results provided in the study cited above are due to a mismatch between the methodology presented by the authors and the code used to conduct the empirical analysis. We empirically support our claims by applying an approximate methodology to the data shared by Dai et al. (2023).

Suggested Citation

  • Bonaccolto, Giovanni & Caporin, Massimiliano & Iacopini, Matteo, 2024. "Extreme time-varying spillovers between high carbon emission stocks, green bond and crude oil: Comment," Energy Economics, Elsevier, vol. 132(C).
    • Handle: RePEc:eee:eneeco:v:132:y:2024:i:c:s0140988324001774
    DOI: 10.1016/j.eneco.2024.107469
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988324001774
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2024.107469?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Antonakakis, Nikolaos & Gabauer, David & Gupta, Rangan & Plakandaras, Vasilios, 2018. "Dynamic connectedness of uncertainty across developed economies: A time-varying approach," Economics Letters, Elsevier, vol. 166(C), pages 63-75.
    2. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    3. Dai, Zhifeng & Zhang, Xiaotong & Yin, Zhujia, 2023. "Extreme time-varying spillovers between high carbon emission stocks, green bond and crude oil: Evidence from a quantile-based analysis," Energy Economics, Elsevier, vol. 118(C).
    4. 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.
    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. Zhongzheng, Wang, 2023. "Extreme risk transmission mechanism between oil, green bonds and new energy vehicles," Innovation and Green Development, Elsevier, vol. 2(3).
    2. Aharon, David Y. & Kizys, Renatas & Umar, Zaghum & Zaremba, Adam, 2023. "Did David win a battle or the war against Goliath? Dynamic return and volatility connectedness between the GameStop stock and the high short interest indices," Research in International Business and Finance, Elsevier, vol. 64(C).
    3. Li, Zhaohua & Hu, Baiding & Zhang, Yuqian & Yang, Wanyi, 2024. "Financial market spillovers and investor attention to the Russia-Ukraine war," International Review of Economics & Finance, Elsevier, vol. 96(PA).
    4. Xu, Danyang & Hu, Yang & Oxley, Les & Lin, Boqiang & He, Yongda, 2025. "Exploring the connectedness between major volatility indexes and worldwide sustainable investments," International Review of Financial Analysis, Elsevier, vol. 97(C).
    5. Hongjun Zeng & Mohammad Zoynul Abedin & Abdullahi D. Ahmed & Brian Lucey, 2025. "Quantile and Time–Frequency Risk Spillover Between Climate Policy Uncertainty and Grains Commodity Markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(6), pages 659-682, June.
    6. Li, Yueshan & Chen, Shoudong & Sensoy, Ahmet & Wang, Lu, 2024. "Over-expected shocks and financial market security: Evidence from China's markets," Research in International Business and Finance, Elsevier, vol. 68(C).
    7. Dai, Zhifeng & Zhang, Xiaotong & Yin, Zhujia, 2023. "Extreme time-varying spillovers between high carbon emission stocks, green bond and crude oil: Evidence from a quantile-based analysis," Energy Economics, Elsevier, vol. 118(C).
    8. Chen, Yan & Wang, Gang-Jin & Zhu, You & Xie, Chi & Uddin, Gazi Salah, 2023. "Quantile connectedness and the determinants between FinTech and traditional financial institutions: Evidence from China," Global Finance Journal, Elsevier, vol. 58(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. Aysan, Ahmet Faruk & Batten, Jonathan & Gozgor, Giray & Khalfaoui, Rabeh & Nanaeva, Zhamal, 2024. "Metaverse and financial markets: A quantile-time-frequency connectedness analysis," Research in International Business and Finance, Elsevier, vol. 72(PB).
    11. Hongjun Zeng & Abdullahi D. Ahmed, 2026. "Dependency structure and volatility connectedness among China-ASEAN stock market, cryptocurrencies, and crude oil," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 12(1), pages 1-39, December.
    12. Gabauer, David & Chatziantoniou, Ioannis & Stenfors, Alexis, 2023. "Model-free connectedness measures," Finance Research Letters, Elsevier, vol. 54(C).
    13. Evrim Mandacı, Pınar & Cagli, Efe Çaglar & Taşkın, Dilvin, 2020. "Dynamic connectedness and portfolio strategies: Energy and metal markets," Resources Policy, Elsevier, vol. 68(C).
    14. Palaios, Panagiotis & Triantafillou, Anna, 2025. "Exploring spillover effects in the four shipping markets: Theory and empirical evidence from bulk shipping," Transport Policy, Elsevier, vol. 170(C), pages 75-91.
    15. Wan, Yang & He, Shi, 2021. "Dynamic connectedness of currencies in G7 countries: A Bayesian time-varying approach," Finance Research Letters, Elsevier, vol. 41(C).
    16. Lei, Heng & Xue, Minggao & Ye, Jing, 2024. "The nexus between ReFi, carbon, fossil energy, and clean energy assets: Quantile time–frequency connectedness and portfolio implications," Energy Economics, Elsevier, vol. 132(C).
    17. Chen, Yu-Fen & Lin, Fu-Lai & Yeh, Wen-Hung, 2024. "Intra- and inter-sector spillover effects within a supply chain: Evidence from Taiwan electric motorcycle industry," Economics Letters, Elsevier, vol. 240(C).
    18. Wang, Xichen, 2025. "The quantile connectedness of the international housing market," Journal of International Money and Finance, Elsevier, vol. 152(C).
    19. Wu, Ran & Li, Ming & Ma, Shenglin & Zeng, Hongjun, 2026. "Asymmetric market connectedness and systemic risk: A novel quantile and time-frequency evidence from brazilian energy firms," Renewable Energy, Elsevier, vol. 260(C).
    20. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2020. "Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions," JRFM, MDPI, vol. 13(4), pages 1-23, April.

    More about this item

    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
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:132:y:2024:i:c:s0140988324001774. 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.