Nicole Horta – ESCE, Instituto Politรฉcnico de Setรบbal, Portugal

Mariana Chambino – ESCE, Instituto Politรฉcnico de Setรบbal, Portugal

Rui Dias – ESCE, Instituto Politรฉcnico de Setรบbal, Portugal; Center for Studies and Advanced Training in Management and Economics (CEFAGE), University of ร‰vora, Portugal

Keywords:
Clean energy;
2020 and 2022 events;
Co-movements;
Diversification of portfolios

DOI: https://doi.org/10.31410/EMAN.2023.101

Abstract: The study of the changes between the Energy Fuels Index, S&P Global Clean Energy Index, iShares Global Clean Energy ETF, iShares Global Energy (SWX) ETF, as well as the changes in the prices of crude oil (BRENT), gold (DJ), and natural gas (DG) was deemed extremely relevant given the imยญportance and emergence of clean energies in the global landscape, as well as the need to develop more empirical studies, especially confirmative studยญies on the financial dynamics in these markets. The daily returns under analยญysis exhibit negative and leptokurtic asymmetry rather than a normal distriยญbution. Comparatively, the pre-crisis linkages between the markets for dirty and clean energy are in favor of global portfolio diversification since those low levels of dependence are appropriate to reduce investor exposure to risk. The crude oil market already exhibited a significant effect on the clean enerยญgy markets during the Stress subperiod, particularly on the Clean Energy Fuels Index, the iShares Global Clean Energy ETF, and the iShares Global Clean Enยญergy (SWX) ETF. It should be highlighted that the clean energy markets have also increased their impact on the markets for gold and dirty energy (crude oil and natural gas). The findings point to an increase in comovements between the examined indices and the events of 2020 and 2022. These results decrease the possibility that clean energy markets will serve as a portfolio diversificaยญtion substitute for the gold and dirty energy markets. For investors and finanยญcial analysts who are interested in understanding how the various sectors of the energy market interact, these results may also have consequences. These results can enable a more precise forecast of energy market trends and more informed investment decisions by offering a more detailed knowledge of the link between clean and dirty energy prices.

Download full paper

7th International Scientific Conference – EMAN 2023 – Economics and Management: How to Cope With Disrupted Times, Ljubljana, Slovenia, March 23, 2023, CONFERENCE PROCEEDINGS, published by: Association of Economists and Managers of the Balkans, Belgrade, Serbia; ISBN 978-86-80194-69-1, ISSN 2683-4510, DOI: https://doi.org/10.31410/EMAN.2023

Creative Commons Nonย Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission.ย 

Suggested citation
Horta, N., Chambino, M., & Dias, R. (2023). Interconnections between Clean Energy and Traditional Commodities: Analysis of Energy Fuels, S&P Global Clean Energy Index, and Ishares Global Clean Energy ETF Compared to Oil, Gold, and Natural Gas Prices. In V. Bevanda (Ed.), International Scientific Conference – EMAN 2023: Vol 7. Conference Proceedingsย (pp. 101-116). Association of Economists and Managers of the Balkans. https://doi.org/10.31410/EMAN.2023.101

REFERENCES

Angelini, E., Birindelli, G., Chiappini, H., & Foglia, M. (2022). Clean energy indexes and brown assets: an analysis of tail risk spillovers through the VAR for VaR model. Journal of Susยญtainable Finance and Investment. https://doi.org/10.1080/20430795.2022.2105788ย 

Basoglu, M. S., Korkmaz, T., & Cevik, E. I. (2014). London metal exchange: Causality relationยญship between the price series of non-ferrous metal contracts. International Journal of Ecoยญnomics and Financial Issues, 4(4).

Breitung, J. (2000). The local power of some unit root tests for panel data. Advances in Econoยญmetrics. https://doi.org/10.1016/S0731-9053(00)15006-6

Chen, J., Wang, Y., & Ren, X. (2022). Asymmetric effects of non-ferrous metal price shocks on clean energy stocks: Evidence from a quantile-on-quantile method. Resources Policy, 78. https://doi.org/10.1016/j.resourpol.2022.102796ย 

Dias, R., Pereira, J. M., & Carvalho, L. C. (2022). Are African Stock Markets Efficient? A Comparaยญtive Analysis Between Six African Markets, the UK, Japan and the USA in the Period of the Panยญdemic. Naลกe Gospodarstvo/Our Economy, 68(1), 35โ€“51. https://doi.org/10.2478/ngoe-2022-0004ย 

Dias, R., & Santos, H. (2020). the Impact of Covid-19 on Exchange Rate Volatility: an Econoยญphysics Approach. 6th LIMEN Conference Proceedings (Part of LIMEN Conference Colยญlection), 6(July), 39โ€“49. https://doi.org/10.31410/limen.2020.39ย 

Dias, R., Teixeira, N., Machova, V., Pardal, P., Horak, J., & Vochozka, M. (2020). Random walks and market efficiency tests: Evidence on US, Chinese and European capital marยญkets within the context of the global Covid-19 pandemic. Oeconomia Copernicana, 11(4). https://doi.org/10.24136/OC.2020.024ย 

Dias, R. T., & Carvalho, L. (2021a). Foreign Exchange Market Shocks in the Context of the Global Pandemic (COVID-19). 359โ€“373. https://doi.org/10.4018/978-1-7998-6643-5.ch020ย 

Dias, R. T., & Carvalho, L. (2021b). The Relationship Between Gold and Stock Markets During the COVID-19 Pandemic. May, 462โ€“475. https://doi.org/10.4018/978-1-7998-6643-5.ch026ย 

Dias, R. T., Pardal, P., Teixeira, N., & Horta, N. R. (2022). Tail Risk and Return Predictabilยญity for Europeโ€™s Capital Markets: An Approach in Periods of the 2020 and 2022 Crises. Advances in Human Resources Management and Organizational Development, 281-298. https://doi.org/10.4018/978-1-6684-5666-8.ch015ย 

Dickey, D., & Fuller, W. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057โ€“1072. https://doi.org/10.2307/1912517ย 

Foglia, M., Angelini, E., & Huynh, T. L. D. (2022). Tail risk connectedness in clean enerยญgy and oil financial market. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04745-wย 

Granger, C. W. J., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of Econometrics, 2(2), 111โ€“120. https://doi.org/10.1016/0304-4076(74)90034-7

Guedes, E. F., Santos, R. P. C., Figueredo, L. H. R., Da Silva, P. A., Dias, R. M. T. S., & Zebende, G. F. (2022). Efficiency and Long-Range Correlation in G-20 Stock Indexes: A Sliding Windows Approach. Fluctuation and Noise Letters. https://doi.org/10.1142/S021947752250033Xย ย 

He, Y. X., Jiao, Z., & Yang, J. (2018). Comprehensive evaluation of global clean energy developยญment index based on the improved entropy method. Ecological Indicators, 88. https://doi.org/10.1016/j.ecolind.2017.12.013ย ย 

Herranz, E. (2017). Unit root tests. In Wiley Interdisciplinary Reviews: Computational Statisยญtics. https://doi.org/10.1002/wics.1396ย 

Horta, N., Dias, R., Revez, C., & Alexandre, P. (2022). CRYPTOCURRENCIES AND G7 CAPIยญTAL MARKETS INTEGRATE IN PERIODS OF EXTREME VOLATILITY ? 10(3), 121โ€“130.

Horta, N., Dias, R., Revez, C., Heliodoro, P., & Alexandre, P. (2022). Spillover and Quantitaยญtive Link Between Cryptocurrency Shocks and Stock Returns: New Evidence From G7 Countries. Balkans Journal of Emerging Trends in Social Sciences, 5(1), 1โ€“14. https://doi.org/10.31410/balkans.jetss.2022.5.1.1-14ย  ย 

Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics. https://doi.org/10.1016/S0304-4076(03)00092-7ย 

Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and seยญrial independence of regression residuals. Economics Letters, 6(3), 255โ€“259. https://doi.org/10.1016/0165-1765(80)90024-5ย ย 

Levin, A., Lin, C.-F., & James Chu, C.-S. (2002). Unit root tests in panel data: asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1-24. https://doi.org/10.1016/s0304-4076(01)00098-7ย ย 

Liu, N., Liu, C., Da, B., Zhang, T., & Guan, F. (2021). Dependence and risk spillovers between green bonds and clean energy markets. Journal of Cleaner Production, 279. https://doi.org/10.1016/j.jclepro.2020.123595ย 

Mighri, Z., Ragoubi, H., Sarwar, S., & Wang, Y. (2022). Quantile Granger causality between US stock market indexes and precious metal prices. Resources Policy, 76. https://doi.org/10.1016/j.resourpol.2022.102595ย ย 

Pandey, D. K., Kumar, R., & Kumari, V. (2023). Glasgow climate pact and the global clean energy index constituent stocks. International Journal of Emerging Markets. https://doi.org/10.1108/IJOEM-05-2022-0815ย ย 

Pardal, P., Dias, R., Teixeira, N., & Horta, N. (2022). The Effects of Russiaโ€™ s 2022 Invasion of Ukraine on Global Markets : An Analysis of Particular Capital and Foreign Exchange Markets. https://doi.org/10.4018/978-1-6684-5666-8.ch014ย 

Pardal, P., Dias, R. T., Santos, H., & Vasco, C. (2021). Central European Banking Sector Inteยญgration and Shocks During the Global Pandemic (COVID-19). June, 272โ€“288. https://doi.org/10.4018/978-1-7998-6926-9.ch015ย 

Perron, P., & Phillips, P. C. B. (1988). Testing for a Unit Root in a Time Series Regression. Biยญometrika, 2(75), 335โ€“346. https://doi.org/10.1080/07350015.1992.10509923ย 

Reboredo, J. C., & Ugolini, A. (2018). The impact of energy prices on clean energy stock pricยญes. A multivariate quantile dependence approach. Energy Economics, 76. https://doi.org/10.1016/j.eneco.2018.10.012ย ย 

Shafiullah, M., Chaudhry, S. M., Shahbaz, M., & Reboredo, J. C. (2021). Quantile causality and dependence between crude oil and precious metal prices. International Journal of Finance and Economics, 26(4). https://doi.org/10.1002/ijfe.2119ย 

Tahai, A., Rutledge, R. W., & Karim, K. E. (2004). An examination of financial integration for the group of seven (G7) industrialized countries using an I (2) cointegration model. Apยญplied Financial Economics, 14(5), 327โ€“335. https://doi.org/10.1080/0960310042000211597ย 

Teixeira, N., Dias, R., & Pardal, P. (2022). The gold market as a safe haven when stock markets exhibit pronounced levels of risk : evidence during the China crisis and the COVID-19 pandemic. April, 27โ€“42.

Teixeira, N., Dias, R. T., Pardal, P., & Horta, N. R. (2022). Financial Integration and Comoveยญments Between Capital Markets and Oil Markets: An Approach During the Russian Invaยญsion of Ukraine in 2022. Advances in Human Resources Management and Organizational Development, 240-261. https://doi.org/10.4018/978-1-6684-5666-8.ch013ย 

Tiwari, A. K., Abakah, E. J. A., Yaya, O. O. S., & Appiah, K. O. (2023). Tail risk dependence, co-movement and predictability between green bond and green stocks. Applied Economยญics, 55(2). https://doi.org/10.1080/00036846.2022.2085869ย 

Yahya, M., Ghosh, S., Kanjilal, K., Dutta, A., & Uddin, G. S. (2020). Evaluation of cross-quanยญtile dependence and causality between non-ferrous metals and clean energy indexes. Enยญergy, 202. https://doi.org/10.1016/j.energy.2020.117777ย 

Share this

Association of Economists and Managers of the Balkans โ€“ UdEkoM Balkan
179 Ustanicka St, 11000 Belgrade, Serbia

https://www.udekom.org.rs/home

Udekom Balkans is a dynamic non-governmental and non-profit organization, established in 2014 with a mission to foster the growth of scientific knowledge within the Balkan region and beyond. Our primary objectives include advancing the fields of management and economics, as well as providing educational resources to our members and the wider public.

Who We Are: Our members include esteemed university professors from various scientific disciplines, postgraduate students, and experts from ministries, public administrations, private and public enterprises, multinational corporations, associations, and similar organizations.

Building Bridges Together: Over the course of nine years since our establishment, the Association of Economists and Managers of the Balkans has established impactful partnerships with more than 1,000 diverse institutions across the Balkan region and worldwide.

EMAN conference publications are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.