IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v128y2023ics0140988323006813.html
   My bibliography  Save this article

The connectedness of Energy Transition Metals

Author

Listed:
  • Bastianin, Andrea
  • Casoli, Chiara
  • Galeotti, Marzio

Abstract

We assess the degree of connectedness among 16 metals that are critical for the production of clean energy technologies. These commodities are the constituents of the Energy Transition Metals (ETMs) price index maintained by the International Monetary Fund and comprise base, precious, and minor metals. We rely on Vector Autoregressive models and generalised forecast error variance decomposition to quantify spillovers among ETMs returns and volatilities. By calculating both static and dynamic measures of connectedness, we gain insight into the patterns of shock transmission between ETMs. Our static analysis reveals that base and precious metals are net shock transmitters, while minor and most battery metals are net receivers. By splitting the analysis into three groups, we find that almost half of the connectedness originates within each group, whereas the other half is due to cross-group spillovers. Moreover, we find that the system-wide connectedness of returns is positively correlated with proxies of economic activity, whereas volatility connectedness seems to be more related to global economic policy uncertainty.

Suggested Citation

  • Bastianin, Andrea & Casoli, Chiara & Galeotti, Marzio, 2023. "The connectedness of Energy Transition Metals," Energy Economics, Elsevier, vol. 128(C).
  • Handle: RePEc:eee:eneeco:v:128:y:2023:i:c:s0140988323006813
    DOI: 10.1016/j.eneco.2023.107183
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988323006813
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2023.107183?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 look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Jozef Baruník & Evžen KoÄ enda b,a & Lukáš Vácha, 2016. "Volatility Spillovers Across Petroleum Markets," The Energy Journal, , vol. 37(1), pages 136-158, January.
    2. Bastianin, Andrea & Galeotti, Marzio & Manera, Matteo, 2017. "Oil supply shocks and economic growth in the Mediterranean," Energy Policy, Elsevier, vol. 110(C), pages 167-175.
    3. Chen, Ying & Zhu, Xuehong & Chen, Jinyu, 2022. "Spillovers and hedging effectiveness of non-ferrous metals and sub-sectoral clean energy stocks in time and frequency domain," Energy Economics, Elsevier, vol. 111(C).
    4. 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.
    5. James D. Hamilton, 2009. "Causes and Consequences of the Oil Shock of 2007-08," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 40(1 (Spring), pages 215-283.
    6. Caldara, Dario & Iacoviello, Matteo & Molligo, Patrick & Prestipino, Andrea & Raffo, Andrea, 2020. "The economic effects of trade policy uncertainty," Journal of Monetary Economics, Elsevier, vol. 109(C), pages 38-59.
    7. 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.
    8. 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.
    9. García, Carlos J. & González, Wildo D., 2013. "Exchange rate intervention in small open economies: The role of risk premium and commodity price shocks," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 424-447.
    10. Considine, Jennifer & Galkin, Phillip & Hatipoglu, Emre & Aldayel, Abdullah, 2023. "The effects of a shock to critical minerals prices on the world oil price and inflation," Energy Economics, Elsevier, vol. 127(PB).
    11. Daron Acemoglu & Vasco M. Carvalho & Asuman Ozdaglar & Alireza Tahbaz‐Salehi, 2012. "The Network Origins of Aggregate Fluctuations," Econometrica, Econometric Society, vol. 80(5), pages 1977-2016, September.
    12. Atsushi Sekine & Takayuki Tsuruga, 2018. "Effects of commodity price shocks on inflation: a cross-country analysis," Oxford Economic Papers, Oxford University Press, vol. 70(4), pages 1108-1135.
    13. Christiane Baumeister & Dimitris Korobilis & Thomas K. Lee, 2022. "Energy Markets and Global Economic Conditions," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 828-844, October.
    14. Nicholson, William B. & Matteson, David S. & Bien, Jacob, 2017. "VARX-L: Structured regularization for large vector autoregressions with exogenous variables," International Journal of Forecasting, Elsevier, vol. 33(3), pages 627-651.
    15. Thibault Fally & James Sayre, 2018. "Commodity Trade Matters," 2018 Meeting Papers 172, Society for Economic Dynamics.
    16. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    17. Baruník, Jozef & Kočenda, Evžen & Vácha, Lukáš, 2016. "Asymmetric connectedness on the U.S. stock market: Bad and good volatility spillovers," Journal of Financial Markets, Elsevier, vol. 27(C), pages 55-78.
    18. Hadi Salehi Esfahani & Kamiar Mohaddes & M. Hashem Pesaran, 2014. "An Empirical Growth Model For Major Oil Exporters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 1-21, January.
    19. Lutz Kilian, 2008. "The Economic Effects of Energy Price Shocks," Journal of Economic Literature, American Economic Association, vol. 46(4), pages 871-909, December.
    20. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
    21. Peersman, Gert & Van Robays, Ine, 2012. "Cross-country differences in the effects of oil shocks," Energy Economics, Elsevier, vol. 34(5), pages 1532-1547.
    22. Zhu, Xuening & Wang, Weining & Wang, Hansheng & Härdle, Wolfgang Karl, 2019. "Network quantile autoregression," Journal of Econometrics, Elsevier, vol. 212(1), pages 345-358.
    23. Steven J. Davis, 2016. "An Index of Global Economic Policy Uncertainty," NBER Working Papers 22740, National Bureau of Economic Research, Inc.
    24. Matteo Barigozzi & Christian Brownlees, 2019. "NETS: Network estimation for time series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(3), pages 347-364, April.
    25. M. Hakan Berument & Nildag Basak Ceylan & Nukhet Dogan, 2010. "The Impact of Oil Price Shocks on the Economic Growth of Selected MENA1 Countries," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 149-176.
    26. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    27. Maryam Ahmadi & Matteo Manera, 2021. "Oil Price Shocks and Economic Growth in Oil-Exporting Countries," Working Papers 2021.13, Fondazione Eni Enrico Mattei.
    28. Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Commodity Connectedness," Central Banking, Analysis, and Economic Policies Book Series, in: Enrique G. Mendoza & Ernesto Pastén & Diego Saravia (ed.),Monetary Policy and Global Spillovers: Mechanisms, Effects and Policy Measures, edition 1, volume 25, chapter 4, pages 097-136, Central Bank of Chile.
    29. Francis X. Diebold & Kamil Yilmaz, 2022. "On the Past, Present, and Future of the Diebold-Yilmaz Approach to Dynamic Network Connectedness," Koç University-TUSIAD Economic Research Forum Working Papers 2207, Koc University-TUSIAD Economic Research Forum.
    30. Jozef Baruník & Tobias Kley, 2019. "Quantile coherency: A general measure for dependence between cyclical economic variables," The Econometrics Journal, Royal Economic Society, vol. 22(2), pages 131-152.
    31. 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.
    32. 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.
    33. Gilbert E. Metcalf, 2014. "The Economics of Energy Security," Annual Review of Resource Economics, Annual Reviews, vol. 6(1), pages 155-174, October.
    34. Matteo Barigozzi & Giuseppe Cavaliere & Graziano Moramarco, 2022. "Factor Network Autoregressions," Papers 2208.02925, arXiv.org, revised Feb 2024.
    35. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    36. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Ebens, Heiko, 2001. "The distribution of realized stock return volatility," Journal of Financial Economics, Elsevier, vol. 61(1), pages 43-76, July.
    37. Éléonore Lèbre & Martin Stringer & Kamila Svobodova & John R. Owen & Deanna Kemp & Claire Côte & Andrea Arratia-Solar & Rick K. Valenta, 2020. "The social and environmental complexities of extracting energy transition metals," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    38. Su, Chi Wei & Shao, Xuefeng & Jia, Zhijie & Nepal, Rabindra & Umar, Muhammad & Qin, Meng, 2023. "The rise of green energy metal: Could lithium threaten the status of oil?," Energy Economics, Elsevier, vol. 121(C).
    39. Gianluca Benigno & Julian di Giovanni & Jan J. J. Groen & Adam I. Noble, 2022. "The GSCPI: A New Barometer of Global Supply Chain Pressures," Staff Reports 1017, Federal Reserve Bank of New York.
    40. Tidiane Kinda & Montfort Mlachila & Rasmane Ouedraogo, 2018. "Do commodity price shocks weaken the financial sector?," The World Economy, Wiley Blackwell, vol. 41(11), pages 3001-3044, November.
    41. Mathieu Jacomy & Tommaso Venturini & Sebastien Heymann & Mathieu Bastian, 2014. "ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-12, June.
    42. Wang, Lu & Guan, Li & Ding, Qian & Zhang, Hongwei, 2023. "Asymmetric impact of COVID-19 news on the connectedness of the green energy, dirty energy, and non-ferrous metal markets," Energy Economics, Elsevier, vol. 126(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. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Filis, George, 2017. "Oil shocks and stock markets: Dynamic connectedness under the prism of recent geopolitical and economic unrest," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 1-26.
    2. Caporin, Massimiliano & Naeem, Muhammad Abubakr & Arif, Muhammad & Hasan, Mudassar & Vo, Xuan Vinh & Hussain Shahzad, Syed Jawad, 2021. "Asymmetric and time-frequency spillovers among commodities using high-frequency data," Resources Policy, Elsevier, vol. 70(C).
    3. Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Estimating global bank network connectedness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(1), pages 1-15, January.
    4. Wang, Gang-Jin & Xie, Chi & Zhao, Longfeng & Jiang, Zhi-Qiang, 2018. "Volatility connectedness in the Chinese banking system: Do state-owned commercial banks contribute more?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 57(C), pages 205-230.
    5. 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.
    6. Apergis, Nicholas & Baruník, Jozef & Lau, Marco Chi Keung, 2017. "Good volatility, bad volatility: What drives the asymmetric connectedness of Australian electricity markets?," Energy Economics, Elsevier, vol. 66(C), pages 108-115.
    7. Wang, Gang-Jin & Chen, Yang-Yang & Si, Hui-Bin & Xie, Chi & Chevallier, Julien, 2021. "Multilayer information spillover networks analysis of China’s financial institutions based on variance decompositions," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 325-347.
    8. Abricha, Amal & Ben Amar, Amine & Bellalah, Makram, 2024. "Commodity futures markets under stress and stress-free periods: Further insights from a quantile connectedness approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 93(C), pages 229-246.
    9. Amar, Amine Ben & Goutte, Stéphane & Isleimeyyeh, Mohammad & Benkraiem, Ramzi, 2022. "Commodity markets dynamics: What do cross-commodities over different nearest-to-maturities tell us?," International Review of Financial Analysis, Elsevier, vol. 82(C).
    10. Elsayed, Ahmed H. & Hammoudeh, Shawkat & Sousa, Ricardo M., 2021. "Inflation synchronization among the G7and China: The important role of oil inflation," Energy Economics, Elsevier, vol. 100(C).
    11. Baruník, Jozef & Kočenda, Evžen & Vácha, Lukáš, 2017. "Asymmetric volatility connectedness on the forex market," Journal of International Money and Finance, Elsevier, vol. 77(C), pages 39-56.
    12. Wen, Tiange & Wang, Gang-Jin, 2020. "Volatility connectedness in global foreign exchange markets," Journal of Multinational Financial Management, Elsevier, vol. 54(C).
    13. Shang, Jin & Hamori, Shigeyuki, 2024. "Quantile time-frequency connectedness analysis between crude oil, gold, financial markets, and macroeconomic indicators: Evidence from the US and EU," Energy Economics, Elsevier, vol. 132(C).
    14. Chan, Ying Tung & Qiao, Hui, 2023. "Volatility spillover between oil and stock prices: Structural connectedness based on a multi-sector DSGE model approach with Bayesian estimation," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 265-286.
    15. Li, Wenqi, 2021. "COVID-19 and asymmetric volatility spillovers across global stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    16. Cheng, Tingting & Liu, Junli & Yao, Wenying & Zhao, Albert Bo, 2022. "The impact of COVID-19 pandemic on the volatility connectedness network of global stock market," Pacific-Basin Finance Journal, Elsevier, vol. 71(C).
    17. Wang, Kangsheng & Wen, Fenghua & Gong, Xu, 2024. "Oil prices and systemic financial risk: A complex network analysis," Energy, Elsevier, vol. 293(C).
    18. Apostolakis, George N. & Floros, Christos & Gkillas, Konstantinos & Wohar, Mark, 2021. "Financial stress, economic policy uncertainty, and oil price uncertainty," Energy Economics, Elsevier, vol. 104(C).
    19. Gabauer, David & Chatziantoniou, Ioannis & Stenfors, Alexis, 2023. "Model-free connectedness measures," Finance Research Letters, Elsevier, vol. 54(C).
    20. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2020. "Dynamic frequency connectedness between oil and natural gas volatilities," Economic Modelling, Elsevier, vol. 84(C), pages 181-189.

    More about this item

    Keywords

    Connectedness; Energy Transition; Metals; Raw materials;
    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
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

    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:128:y:2023:i:c:s0140988323006813. 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.