Unearthing the Potential: Energy Metals as Hedging Assets in Portfolio Rebalancing Strategies

Rui Dias – Polytechnic Institute of Setúbal, ESCE, Portugal

Mariana Chambino –Polytechnic Institute of Setúbal, ESCE, Portugal

Paulo Alexandre – Polytechnic Institute of Setúbal, ESCE, Portugal

Abstract: This article analyzes energy metals, specifically Aluminum (MAL3), nickel futures (NICKELc1), and copper futures (HGU3), concerning 3 clean ener­gy indexes: the S&P Global Clean Energy Index (SPGTCLEN), the Nasdaq Clean Edge Green Energy (CEXX), and the WilderHill Clean Energy Index (ECO). It fo­cuses on a significant period during which investments in clean energy in­creased significantly, with capital allocations tripling from the previous de­cade. The study’s findings are of great significance, given the recent surge in clean energy investments. As the world increasingly embraces cleaner ener­gy sources, understanding how these assets relate to clean energy indexes be­comes crucial for investors navigating this dynamic landscape. Moreover, the study’s relevance is underscored by recurring market uncertainty, making it a valuable resource for investors seeking to make informed decisions and man­age portfolio risks in an era marked by economic uncertainty, policy shifts, and environmental concerns. This work contributes to academic discourse and has practical implications for financial markets.

7th International Scientific Conference on Recent Advances in Information Technology, Tourism, Economics, Management and Agriculture – ITEMA 2023 – Conference Proceedings, Hybrid (Faculty of Organization and Informatics Varaždin, University of Zagreb, Croatia), October 26, 2023

ITEMA Conference Proceedings published by: Association of Economists and Managers of the Balkans – Belgrade, Serbia

ITEMA conference partners: Faculty of Economics and Business, University of Maribor, Slovenia; Faculty of Organization and Informatics, University of Zagreb, Varaždin; Faculty of Geography, University of Belgrade, Serbia; Institute of Marketing, Poznan University of Economics and Business, Poland; Faculty of Agriculture, Banat’s University of Agricultural Sciences and Veterinary Medicine ”King Michael I of Romania”, Romania

ITEMA Conference 2023 Conference Proceedings: ISBN 978-86-80194-75-2, ISSN 2683-5991, DOI: https://doi.org/10.31410/ITEMA.2023

Creative Commons Non 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.

References

Ahmad, W., Sadorsky, P., & Sharma, A. (2018). Optimal hedge ratios for clean energy equities. Economic Modelling, 72, 278–295. https://doi.org/10.1016/J.ECONMOD.2018.02.008 

Asl, M. G., Canarella, G., & Miller, S. M. (2021). Dynamic asymmetric optimal portfolio alloca­tion between energy stocks and energy commodities: Evidence from clean energy and oil and gas companies. Resources Policy, 71. https://doi.org/10.1016/j.resourpol.2020.101982 

Bondia, R., Ghosh, S., & Kanjilal, K. (2016). International crude oil prices and the stock prices of clean energy and technology companies: Evidence from non-linear cointegration tests with unknown structural breaks. Energy, 101. https://doi.org/10.1016/j.energy.2016.02.031 

Breitung, J. (2000). The local power of some unit root tests for panel data. Advances in Economet­rics, 15. https://doi.org/10.1016/S0731-9053(00)15006-6 

Chambino, M., Manuel, R., Dias, T., & Horta, N. R. (2023). Asymmetric efficiency of cryp­tocurrencies during the 2020 and 2022 events. 2(March), 23–33. https://doi.org/10.58567/eal02020004 

Choi, I. (2001). Unit root tests for panel data. Journal of International Money and Finance, 20(2), 249–272. https://doi.org/10.1016/S0261-5606(00)00048-6 

Dias, R., Alexandre, P., Teixeira, N., & Chambino, M. (2023). Clean Energy Stocks: Resilient Safe Havens in the Volatility of Dirty Cryptocurrencies. Energies, 16(13), 5232. https://doi.org/10.3390/en16135232 

Dias, R., Chambino, M., & Horta, N. (2023a). Short-Term Shocks Between Central European Stock Markets: An Approach During The 2020 and 2022 Events. Journal of Economic Anal­ysis. https://doi.org/10.58567/jea02030005 

Dias, R., Chambino, M., & Horta, N. H. (2023b). Long-Term Dependencies in Central European Stock Markets: A Crisp-Set Analysis. Economic Analysis Letters. https://doi.org/10.58567/eal02010002  

Dias, R., Horta, N., & Chambino, M. (2023). Clean Energy Action Index Efficiency: An Analysis in Global Uncertainty Contexts. Energies 2023, 16, 18. https://doi.org/https://doi.org/10.3390/en16093937  

Dias, R., Teixeira, N., Alexandre, P., & Chambino, M. (2023). Exploring the Connection between Clean and Dirty Energy: Implications for the Transition to a Carbon-Resilient Economy. En­ergies, 16(13), 4982. https://doi.org/10.3390/en16134982 

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

Farid, S., Karim, S., Naeem, M. A., Nepal, R., & Jamasb, T. (2023). Co-movement between dirty and clean energy: A time-frequency perspective. Energy Economics, 119. https://doi.org/10.1016/j.eneco.2023.106565 

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/S021947752250033X 

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)00092-7 

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

Kanamura, T. (2022). A model of price correlations between clean energy indices and energy commodities. Journal of Sustainable Finance and Investment, 12(2). https://doi.org/10.1080/20430795.2020.1753434 

Levin, A., Lin, C. F., & Chu, C. S. J. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108(1). https://doi.org/10.1016/S0304-4076(01)00098-7 

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

Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. https://doi.org/10.1093/biomet/75.2.335 

Santana, T., Horta, N., Revez, C., Santos Dias, R. M. T., & Zebende, G. F. (2023). Effects of inter­dependence and contagion between Oil and metals by ρ DCCA : an case of study about the COVID-19. 1–11.

Santana, T. P., Horta, N., Revez, C., Dias, R. M. T. S., & Zebende, G. F. (2023). Effects of Interde­pendence and Contagion on Crude Oil and Precious Metals According to ρDCCA: A COV­ID-19 Case Study. Sustainability (Switzerland), 15(5). https://doi.org/10.3390/su15053945 

Vrînceanu, G., Horobeț, A., Popescu, C., & Belaşcu, L. (2020). The Influence of Oil Price on Re­newable Energy Stock Prices: An Analysis for Entrepreneurs. Studia Universitatis „Vasile Goldis” Arad – Economics Series, 30(2). https://doi.org/10.2478/sues-2020-0010 

Zebende, G. F. (2011). DCCA cross-correlation coefficient: Quantifying level of cross-correla­tion. Physica A: Statistical Mechanics and Its Applications, 390(4). https://doi.org/10.1016/j.physa.2010.10.022 

Zebende, G. F., Santos Dias, R. M. T., & de Aguiar, L. C. (2022). Stock market efficiency: An intr­aday case of study about the G-20 group. In Heliyon (Vol. 8, Issue 1). https://doi.org/10.1016/j.heliyon.2022.e08808