IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v286y2024ics0360544223030049.html
   My bibliography  Save this article

COVID-19 pandemic-related news and Chinese commodities futures: Time-frequency connectedness and causality-in-quantiles approaches

Author

Listed:
  • Chen, Yanan
  • Qi, Haozhi

Abstract

The COVID-19 pandemic has had a significant impact on the global economy and commodity markets, making it essential to study its effects. We aim to learn the dynamic spillovers between COVID-19 pandemic-related news and Chinese commodities futures (Energy, Petrochemical, Non-ferrous metals, Oil & Fats, and Softs) using time-frequency connectedness and causality-in-quantiles approaches. To capture the diverse impacts of different types of COVID-19 news, we utilize a range of COVID-19 news-related indices based on media news data from January 2, 2020, to October 28, 2022. Based on static connectedness data, it is evident that Petrochemical and Energy futures commodities are the primary contributors and recipients in our research samples. The short- and long-term data sets consistently demonstrate that COVID-19 uncertainty is the main recipient, and they have heterogeneous effects on the commodities. Interestingly, the Coronavirus Panic Index and Coronavirus Media Hype Index show the greatest influence on commodities. Dynamic data reveals that the level of connectedness among the markets fluctuates significantly over time, and short-term connectedness is higher than that in the long term. The non-parametric Granger causality test shows that COVID-19 uncertainty can significantly impact commodities futures in China. Specifically, the results remain robust even when utilizing various uncertainty measures and distinct quantiles.

Suggested Citation

  • Chen, Yanan & Qi, Haozhi, 2024. "COVID-19 pandemic-related news and Chinese commodities futures: Time-frequency connectedness and causality-in-quantiles approaches," Energy, Elsevier, vol. 286(C).
    • Handle: RePEc:eee:energy:v:286:y:2024:i:c:s0360544223030049
    DOI: 10.1016/j.energy.2023.129610
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223030049
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.129610?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. Le, TN-Lan & Abakah, Emmanuel Joel Aikins & Tiwari, Aviral Kumar, 2021. "Time and frequency domain connectedness and spill-over among fintech, green bonds and cryptocurrencies in the age of the fourth industrial revolution," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    2. Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch & Mark E. Wohar, 2018. "Terror attacks and stock-market fluctuations: evidence based on a nonparametric causality-in-quantiles test for the G7 countries," The European Journal of Finance, Taylor & Francis Journals, vol. 24(4), pages 333-346, March.
    3. Jiang, Yonghong & Lao, Jiashun & Mo, Bin & Nie, He, 2018. "Dynamic linkages among global oil market, agricultural raw material markets and metal markets: An application of wavelet and copula approaches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 265-279.
    4. Chen, Hao & Xu, Chao, 2022. "The impact of cryptocurrencies on China's carbon price variation during COVID-19: A quantile perspective," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    5. Carter, David A. & Rogers, Daniel A. & Simkins, Betty J. & Treanor, Stephen D., 2017. "A review of the literature on commodity risk management," Journal of Commodity Markets, Elsevier, vol. 8(C), pages 1-17.
    6. Wu, Bi-Bo, 2021. "The dynamics of oil on China’s commodity sectors: What can we learn from a quantile perspective?," Journal of Commodity Markets, Elsevier, vol. 23(C).
    7. Cui, Jinxin & Goh, Mark & Li, Binlin & Zou, Huiwen, 2021. "Dynamic dependence and risk connectedness among oil and stock markets: New evidence from time-frequency domain perspectives," Energy, Elsevier, vol. 216(C).
    8. Qi, Haozhi & Wu, Tiantian & Chen, Hao & Lu, Xiuling, 2023. "Time-frequency connectedness and cross-quantile dependence between carbon emission trading and commodity markets: Evidence from China," Resources Policy, Elsevier, vol. 82(C).
    9. Jiang, Yonghong & Nie, He & Monginsidi, Joe Yohanes, 2017. "Co-movement of ASEAN stock markets: New evidence from wavelet and VMD-based copula tests," Economic Modelling, Elsevier, vol. 64(C), pages 384-398.
    10. Li, Zijian & Meng, Qiaoyu, 2022. "Time and frequency connectedness and portfolio diversification between cryptocurrencies and renewable energy stock markets during COVID-19," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    11. Huber, Christoph & Huber, Jürgen & Kirchler, Michael, 2021. "Market shocks and professionals’ investment behavior – Evidence from the COVID-19 crash," Journal of Banking & Finance, Elsevier, vol. 133(C).
    12. Cai, Guixin & Zhang, Hao & Chen, Ziyue, 2019. "Comovement between commodity sectors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1247-1258.
    13. Lao, Jiashun & Nie, He & Jiang, Yonghong, 2018. "Revisiting the investor sentiment–stock returns relationship: A multi-scale perspective using wavelets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 420-427.
    14. Balcilar, Mehmet & Gupta, Rangan & Pierdzioch, Christian, 2016. "Does uncertainty move the gold price? New evidence from a nonparametric causality-in-quantiles test," Resources Policy, Elsevier, vol. 49(C), pages 74-80.
    15. Khalfaoui, Rabeh & Mefteh-Wali, Salma & Dogan, Buhari & Ghosh, Sudeshna, 2023. "Extreme spillover effect of COVID-19 pandemic-related news and cryptocurrencies on green bond markets: A quantile connectedness analysis," International Review of Financial Analysis, Elsevier, vol. 86(C).
    16. Alberola, Enrique & Benigno, Gianluca, 2017. "Revisiting the commodity curse: A financial perspective," Journal of International Economics, Elsevier, vol. 108(S1), pages 87-106.
    17. Duc Khuong Nguyen & Thomas Walther, 2020. "Modeling and forecasting commodity market volatility with long‐term economic and financial variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 126-142, March.
    18. Balcilar, Mehmet & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2017. "Can volume predict Bitcoin returns and volatility? A quantiles-based approach," Economic Modelling, Elsevier, vol. 64(C), pages 74-81.
    19. He Nie & Yonghong Jiang & Baoqing Yang, 2018. "Do different time horizons in the volatility of the US stock market significantly affect the China ETF market?," Applied Economics Letters, Taylor & Francis Journals, vol. 25(11), pages 747-751, June.
    20. Yang, Dong-Xiao & Wu, Bi-Bo & Tong, Jing-Yang, 2021. "Dynamics and causality of oil price shocks on commodities: Quantile-on-quantile and causality-in-quantiles methods," Resources Policy, Elsevier, vol. 74(C).
    21. Shahbaz, Muhammad & Balcilar, Mehmet & Abidin Ozdemir, Zeynel, 2017. "Does oil predict gold? A nonparametric causality-in-quantiles approach," Resources Policy, Elsevier, vol. 52(C), pages 257-265.
    22. Wang, Zhixuan & Dong, Yanli & Liu, Ailan, 2022. "How does China's stock market react to supply chain disruptions from COVID-19?," International Review of Financial Analysis, Elsevier, vol. 82(C).
    23. Yonghong Jiang & Jinqi Mu & He Nie & Lanxin Wu, 2022. "Time‐frequency analysis of risk spillovers from oil to BRICS stock markets: A long‐memory Copula‐CoVaR‐MODWT method," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3386-3404, July.
    24. Itay Goldstein & Ralph S J Koijen & Holger M Mueller, 2021. "COVID-19 and Its Impact on Financial Markets and the Real Economy [A model of endogenous risk intolerance and LSAPs: Asset prices and aggregate demand in a “COVID-19” shock]," The Review of Financial Studies, Society for Financial Studies, vol. 34(11), pages 5135-5148.
    25. Wang, Jingjing & Wang, Xiaoyang, 2021. "COVID-19 and financial market efficiency: Evidence from an entropy-based analysis," Finance Research Letters, Elsevier, vol. 42(C).
    26. Haroon, Omair & Rizvi, Syed Aun R., 2020. "COVID-19: Media coverage and financial markets behavior—A sectoral inquiry," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    27. Shelby R. Buckman & Adam Hale Shapiro & Moritz Sudhof & Daniel J. Wilson, 2020. "News Sentiment in the Time of COVID-19," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, vol. 2020(08), pages 1-05, April.
    28. Albulescu, Claudiu Tiberiu, 2021. "COVID-19 and the United States financial markets’ volatility," Finance Research Letters, Elsevier, vol. 38(C).
    29. Jiang, Yonghong & Feng, Qidi & Mo, Bin & Nie, He, 2020. "Visiting the effects of oil price shocks on exchange rates: Quantile-on-quantile and causality-in-quantiles approaches," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    30. Yoon, Seong-Min & Al Mamun, Md & Uddin, Gazi Salah & Kang, Sang Hoon, 2019. "Network connectedness and net spillover between financial and commodity markets," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 801-818.
    31. Costola, Michele & Lorusso, Marco, 2022. "Spillovers among energy commodities and the Russian stock market," Journal of Commodity Markets, Elsevier, vol. 28(C).
    32. Mo, Bin & Meng, Juan & Zheng, Liping, 2022. "Time and frequency dynamics of connectedness between cryptocurrencies and commodity markets," Resources Policy, Elsevier, vol. 77(C).
    33. Varron, Davit & Van Keilegom, Ingrid, 2011. "Uniform in bandwidth exact rates for a class of kernel estimators," LIDAM Reprints ISBA 2011016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    34. Davit Varron & Ingrid Van Keilegom, 2011. "Uniform in bandwidth exact rates for a class of kernel estimators," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(6), pages 1077-1102, December.
    35. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
    36. Dietrich Domanski & Alexandra Heath, 2007. "Financial investors and commodity markets," BIS Quarterly Review, Bank for International Settlements, March.
    37. 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).
    38. Jiang, Yonghong & Zhu, Zixuan & Tian, Gengyu & Nie, He, 2019. "Determinants of within and cross-country economic policy uncertainty spillovers: Evidence from US and China," Finance Research Letters, Elsevier, vol. 31(C).
    39. Das, Debojyoti & Kumar, Surya Bhushan & Tiwari, Aviral Kumar & Shahbaz, Muhammad & Hasim, Haslifah M., 2018. "On the relationship of gold, crude oil, stocks with financial stress: A causality-in-quantiles approach," Finance Research Letters, Elsevier, vol. 27(C), pages 169-174.
    40. John, Kose & Li, Jingrui, 2021. "COVID-19, volatility dynamics, and sentiment trading," Journal of Banking & Finance, Elsevier, vol. 133(C).
    41. Tian, Tingting & Lai, Kee-hung & Wong, Christina W.Y., 2022. "Connectedness mechanisms in the “Carbon-Commodity-Finance” system: Investment and management policy implications for emerging economies," Energy Policy, Elsevier, vol. 169(C).
    42. 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).
    43. Jeong, Kiho & Härdle, Wolfgang K. & Song, Song, 2012. "A Consistent Nonparametric Test For Causality In Quantile," Econometric Theory, Cambridge University Press, vol. 28(4), pages 861-887, August.
    44. 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).
    45. Rabeh Khalfaoui & Salma Mefteh-Wali & Buhari Dogan & Sudeshna Ghosh, 2023. "Extreme spillover effect of COVID-19 pandemic-related news and cryptocurrencies on green bond markets: A quantile connectedness analysis," Post-Print hal-03998228, HAL.
    46. Mena, Carlos & Karatzas, Antonios & Hansen, Carsten, 2022. "International trade resilience and the Covid-19 pandemic," Journal of Business Research, Elsevier, vol. 138(C), pages 77-91.
    47. Yun Liu, 2022. "Housing and monetary policy: Fresh evidence from China," Financial Economics Letters, Anser Press, vol. 1(1), pages 1-12, December.
    48. Zhang, Hao & Cai, Guixin & Yang, Dongxiao, 2020. "The impact of oil price shocks on clean energy stocks: Fresh evidence from multi-scale perspective," Energy, Elsevier, vol. 196(C).
    49. Jiang, Yonghong & Jiang, Cheng & Nie, He & Mo, Bin, 2019. "The time-varying linkages between global oil market and China's commodity sectors: Evidence from DCC-GJR-GARCH analyses," Energy, Elsevier, vol. 166(C), pages 577-586.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fu, Yaping & Qi, Haozhi & Chen, Yanan & Wang, Yuzhan, 2024. "Short-term impacts vs. long-term contributions: The role of clean energy and ESG investments in China," Renewable Energy, Elsevier, vol. 233(C).
    2. Chen, Yanan & Qi, Haozhi, 2024. "Dynamic interplay between Chinese energy, renewable energy stocks, and commodity markets: Time-frequency causality study," Renewable Energy, Elsevier, vol. 228(C).

    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. Qi, Haozhi & Ma, Lijun & Peng, Pin & Chen, Hao & Li, Kang, 2022. "Dynamic connectedness between clean energy stock markets and energy commodity markets during times of COVID-19: Empirical evidence from China," Resources Policy, Elsevier, vol. 79(C).
    2. Chen, Yanan & Qi, Haozhi, 2024. "Dynamic interplay between Chinese energy, renewable energy stocks, and commodity markets: Time-frequency causality study," Renewable Energy, Elsevier, vol. 228(C).
    3. Fu, Yaping & Qi, Haozhi & Chen, Yanan & Wang, Yuzhan, 2024. "Short-term impacts vs. long-term contributions: The role of clean energy and ESG investments in China," Renewable Energy, Elsevier, vol. 233(C).
    4. Qi, Haozhi & Wu, Tiantian & Chen, Hao & Lu, Xiuling, 2023. "Time-frequency connectedness and cross-quantile dependence between carbon emission trading and commodity markets: Evidence from China," Resources Policy, Elsevier, vol. 82(C).
    5. 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).
    6. Yang, Dong-Xiao & Wu, Bi-Bo & Tong, Jing-Yang, 2021. "Dynamics and causality of oil price shocks on commodities: Quantile-on-quantile and causality-in-quantiles methods," Resources Policy, Elsevier, vol. 74(C).
    7. Mo, Bin & Nie, He & Zhao, Rongjie, 2024. "Dynamic nonlinear effects of geopolitical risks on commodities: Fresh evidence from quantile methods," Energy, Elsevier, vol. 288(C).
    8. Li, Zhenghui & Mo, Bin & Nie, He, 2023. "Time and frequency dynamic connectedness between cryptocurrencies and financial assets in China," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 46-57.
    9. Mo, Bin & Zeng, Haiyu & Meng, Juan & Ding, Shaokai, 2024. "The connectedness between uncertainty and exchange rates of oil import countries: new evidence from time and frequency perspective," Resources Policy, Elsevier, vol. 88(C).
    10. Chen, Hao & Xu, Chao, 2022. "The impact of cryptocurrencies on China's carbon price variation during COVID-19: A quantile perspective," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    11. Wu, Bi-Bo, 2021. "The dynamics of oil on China’s commodity sectors: What can we learn from a quantile perspective?," Journal of Commodity Markets, Elsevier, vol. 23(C).
    12. Jiang, Yonghong & Feng, Qidi & Mo, Bin & Nie, He, 2020. "Visiting the effects of oil price shocks on exchange rates: Quantile-on-quantile and causality-in-quantiles approaches," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    13. Jiang, Yonghong & Lie, Jiayi & Wang, Jieru & Mu, Jinqi, 2021. "Revisiting the roles of cryptocurrencies in stock markets: A quantile coherency perspective," Economic Modelling, Elsevier, vol. 95(C), pages 21-34.
    14. Chen, Qitong & Zhu, Huiming & Yu, Dongwei & Hau, Liya, 2022. "How does investor attention matter for crude oil prices and returns? Evidence from time-frequency quantile causality analysis," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    15. Liu, Jianjian & Wang, Shuhan & Xiang, Lijin & Ma, Shiqun & Xiao, Zumian, 2024. "Unveiling hidden connections: Spillover among BRICS' cryptocurrency-implied exchange rate discounts and US financial markets," The North American Journal of Economics and Finance, Elsevier, vol. 71(C).
    16. Zou, Fei & Huang, Lingyu & Ghaemi Asl, Mahdi & Delnavaz, Mohammad & Tiwari, Sunil, 2023. "Natural resources and green economic recovery in responsible investments: Role of ESG in context of Islamic sustainable investments," Resources Policy, Elsevier, vol. 86(PA).
    17. Lu, Xunfa & Huang, Nan & Mo, Jianlei, 2024. "Time-varying causalities from the COVID-19 media coverage to the dynamic spillovers among the cryptocurrency, the clean energy, and the crude oil," Energy Economics, Elsevier, vol. 132(C).
    18. Vladimir Balash & Alexey Faizliev & Sergei Sidorov & Elena Chistopolskaya, 2021. "Conditional Time-Varying General Dynamic Factor Models and Its Application to the Measurement of Volatility Spillovers across Russian Assets," Mathematics, MDPI, vol. 9(19), pages 1-31, October.
    19. Fasanya, Ismail O. & Adekoya, Oluwasegun B. & Adetokunbo, Abiodun M., 2021. "On the connection between oil and global foreign exchange markets: The role of economic policy uncertainty," Resources Policy, Elsevier, vol. 72(C).
    20. Zhu, Huiming & Chen, Yiwen & Ren, Yinghua & Xing, Zhanming & Hau, Liya, 2022. "Time-frequency causality and dependence structure between crude oil, EPU and Chinese industry stock: Evidence from multiscale quantile perspectives," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).

    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:energy:v:286:y:2024:i:c:s0360544223030049. 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.journals.elsevier.com/energy .

    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.