IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i23p6173-d1538847.html
   My bibliography  Save this article

Market Risk of Lithium Industry Chain—Evidence from Listed Companies

Author

Listed:
  • Weicheng Kong

    (School of Economics & Management, China University of Geosciences, Wuhan 430078, China)

  • Jinhua Cheng

    (School of Economics & Management, China University of Geosciences, Wuhan 430078, China)

  • Jianzhong Xiao

    (School of Economics & Management, China University of Geosciences, Wuhan 430078, China)

Abstract

Lithium, a crucial raw material for new energy vehicles, is experiencing significant market price fluctuations due to escalating geopolitical conflicts, periodic mismatches in supply and demand, and increased attention to lithium resources from countries around the world. These factors may adversely affect the development of the new energy vehicle industry. This paper adopts the TVP-VAR-DY model, which measures dynamic spillover effects by allowing for variance changes through the estimation of a stochastic Kalman filter, thereby measuring risk spillover among upstream and downstream firms in the lithium industry chain. We selected 16 listed companies and six regional financial markets as the research sample, with the sample period from 4 July 2018, to 30 June 2023. The main conclusions are as follows: Between 2018 and 2020, the overall risk spillover in the lithium industry chain demonstrated a declining trend, though it experienced a sudden surge in 2020 as a result of the COVID-19 pandemic. This increase was followed by a gradual decline as the global economy improved and market stability was restored, leading to a reduction in risk aversion. Regarding the reception of risk spillovers, upstream firms exhibited a generally consistent level of directional risk spillovers, whereas downstream firms experienced more significant fluctuations. Chinese firms exhibited a higher level of received risk spillovers compared to their international counterparts, with less variation in these spillovers. From the perspective of risk spillover effects, significant variations were observed between firms in both the upstream and downstream markets. Chinese firms exhibited a higher level of risk inflow than international firms, with more pronounced changes in risk spillovers. Upstream enterprises should enhance their market competitiveness to mitigate the adverse effects of economic uncertainty. Downstream enterprises can alleviate the rise in raw material costs resulting from market price fluctuations through strategic cooperation. Additionally, the government should increase the market supply of resources, which will contribute to the establishment of a more robust lithium industry chain system.

Suggested Citation

  • Weicheng Kong & Jinhua Cheng & Jianzhong Xiao, 2024. "Market Risk of Lithium Industry Chain—Evidence from Listed Companies," Energies, MDPI, vol. 17(23), pages 1-24, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:6173-:d:1538847
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/23/6173/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/23/6173/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. De Bruyckere, Valerie & Gerhardt, Maria & Schepens, Glenn & Vander Vennet, Rudi, 2013. "Bank/sovereign risk spillovers in the European debt crisis," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 4793-4809.
    2. Restrepo, Natalia & Ceballos, Juan Camilo & Uribe, Jorge M., 2023. "Risk spillovers of critical metals firms," Resources Policy, Elsevier, vol. 86(PB).
    3. Balcılar, Mehmet & Demirer, Rıza & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2016. "Risk spillovers across the energy and carbon markets and hedging strategies for carbon risk," Energy Economics, Elsevier, vol. 54(C), pages 159-172.
    4. Cao, Yufei, 2022. "Extreme risk spillovers across financial markets under different crises," Economic Modelling, Elsevier, vol. 116(C).
    5. repec:ocp:rpcoen:pb_37-22 is not listed on IDEAS
    6. Wen, Danyan & Wang, Gang-Jin & Ma, Chaoqun & Wang, Yudong, 2019. "Risk spillovers between oil and stock markets: A VAR for VaR analysis," Energy Economics, Elsevier, vol. 80(C), pages 524-535.
    7. repec:ocp:ppaper:pb37-22 is not listed on IDEAS
    8. Rim Berahab, 2022. "The Energy Transition Amidst Global Uncertainties: A Focus on Critical Minerals," Policy briefs on Commodities & Energy 2216, Policy Center for the New South.
    9. Corbet, Shaen & Goodell, John W. & Günay, Samet, 2020. "Co-movements and spillovers of oil and renewable firms under extreme conditions: New evidence from negative WTI prices during COVID-19," Energy Economics, Elsevier, vol. 92(C).
    10. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    11. 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.
    12. Ferrer, Román & Shahzad, Syed Jawad Hussain & López, Raquel & Jareño, Francisco, 2018. "Time and frequency dynamics of connectedness between renewable energy stocks and crude oil prices," Energy Economics, Elsevier, vol. 76(C), pages 1-20.
    13. Du, Limin & He, Yanan, 2015. "Extreme risk spillovers between crude oil and stock markets," Energy Economics, Elsevier, vol. 51(C), pages 455-465.
    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. Hoque, Mohammad Enamul & Soo-Wah, Low & Billah, Mabruk, 2023. "Time-frequency connectedness and spillover among carbon, climate, and energy futures: Determinants and portfolio risk management implications," Energy Economics, Elsevier, vol. 127(PB).
    2. Benlagha, Noureddine & Karim, Sitara & Naeem, Muhammad Abubakr & Lucey, Brian M. & Vigne, Samuel A., 2022. "Risk connectedness between energy and stock markets: Evidence from oil importing and exporting countries," Energy Economics, Elsevier, vol. 115(C).
    3. Zhou, Wei & Chen, Yan & Chen, Jin, 2022. "Risk spread in multiple energy markets: Extreme volatility spillover network analysis before and during the COVID-19 pandemic," Energy, Elsevier, vol. 256(C).
    4. Zhang, Hua & Chen, Jinyu & Shao, Liuguo, 2021. "Dynamic spillovers between energy and stock markets and their implications in the context of COVID-19," International Review of Financial Analysis, Elsevier, vol. 77(C).
    5. Feng, Huiqun & Zhang, Jun & Guo, Na, 2023. "Time-varying linkages between energy and stock markets: Dynamic spillovers and driving factors," International Review of Financial Analysis, Elsevier, vol. 89(C).
    6. Sevillano, María Caridad & Jareño, Francisco & López, Raquel & Esparcia, Carlos, 2024. "Connectedness between oil price shocks and US sector returns: Evidence from TVP-VAR and wavelet decomposition," Energy Economics, Elsevier, vol. 131(C).
    7. Chen, Jianyu & Zhang, Jianshun, 2023. "Crude oil price shocks, volatility spillovers, and global systemic financial risk transmission mechanisms: Evidence from the stock and foreign exchange markets," Resources Policy, Elsevier, vol. 85(PB).
    8. Su, Chi-Wei & Pang, Li-Dong & Qin, Meng & Lobonţ, Oana-Ramona & Umar, Muhammad, 2023. "The spillover effects among fossil fuel, renewables and carbon markets: Evidence under the dual dilemma of climate change and energy crises," Energy, Elsevier, vol. 274(C).
    9. Wen, Danyan & Wang, Yudong, 2021. "Volatility linkages between stock and commodity markets revisited: Industry perspective and portfolio implications," Resources Policy, Elsevier, vol. 74(C).
    10. Yousaf, Imran & Arfaoui, Nadia & Gubareva, Mariya, 2024. "Spillovers and hedging effectiveness between oil and US equity sectors: Evidence from the COVID pre- and post-vaccination phases," Research in International Business and Finance, Elsevier, vol. 69(C).
    11. Matteo Foglia & Eliana Angelini, 2020. "Volatility Connectedness between Clean Energy Firms and Crude Oil in the COVID-19 Era," Sustainability, MDPI, vol. 12(23), pages 1-22, November.
    12. Corbet, Shaen & Hou, Yang (Greg) & Hu, Yang & Oxley, Les, 2021. "Volatility spillovers during market supply shocks: The case of negative oil prices," Resources Policy, Elsevier, vol. 74(C).
    13. Ahonen, Elena & Corbet, Shaen & Goodell, John W. & Günay, Samet & Larkin, Charles, 2022. "Are carbon futures prices stable? New evidence during negative oil," Finance Research Letters, Elsevier, vol. 47(PB).
    14. Zeng, Sheng & Liu, Xinchun & Li, Xiafei & Wei, Qi & Shang, Yue, 2019. "Information dominance among hedging assets: Evidence from return and volatility directional spillovers in time and frequency domains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    15. Xie, Qichang & Fang, Tingwei & Rong, Xueyun & Xu, Xin, 2024. "Nonlinear behavior of tail risk resonance and early warning: Insight from global energy stock markets," International Review of Financial Analysis, Elsevier, vol. 93(C).
    16. Wan, Jieru & Yin, Libo & Wu, You, 2024. "Return and volatility connectedness across global ESG stock indexes: Evidence from the time-frequency domain analysis," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 397-428.
    17. Corbet, Shaen & Goodell, John W. & Günay, Samet, 2020. "Co-movements and spillovers of oil and renewable firms under extreme conditions: New evidence from negative WTI prices during COVID-19," Energy Economics, Elsevier, vol. 92(C).
    18. Cui, Jinxin & Goh, Mark & Zou, Huiwen, 2021. "Coherence, extreme risk spillovers, and dynamic linkages between oil and China’s commodity futures markets," Energy, Elsevier, vol. 225(C).
    19. Joanna Górka & Katarzyna Kuziak, 2024. "Dynamic Connectedness Among Alternative and Conventional Energy ETFs Based on the TVP-VAR Approach," Energies, MDPI, vol. 17(23), pages 1-29, November.
    20. 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).

    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:gam:jeners:v:17:y:2024:i:23:p:6173-:d:1538847. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.