Rui Dias – Polytechnic Institute of SetΓΊbal (ESCE/IPS), 2910-761 SetΓΊbal, Portugal
Mariana Chambino – Polytechnic Institute of SetΓΊbal (ESCE/IPS), 2910-761 SetΓΊbal, Portugal
Paulo Alexandre – Polytechnic Institute of SetΓΊbal (ESCE/IPS), 2910-761 SetΓΊbal, Portugal
Rosa GalvΓ£o – Polytechnic Institute of SetΓΊbal (ESCE/IPS), 2910-761 SetΓΊbal, Portugal
Keywords:
Green economy;
Connectedness;
Hedge assets
Abstract: The pursuit of financing instruments aligned with the SustainΒable Development Goals (SDGs) is crucial for fostering sustainability in diΒverse economic sectors. However, a significant gap exists in understanding the performance of green assets, posing challenges for investors and polΒicymakers. This study addresses this gap by examining the relationships among green energy indexes, including ISE Clean Edge Global Wind Energy, S&P Global Clean Energy, S&P TSX Renewable Energy and Clean TechnoloΒgy, and Solactive China Clean Energy, from January 2020 to October 2023. Findings reveal that ISE Clean Edge and S&P Global Clean Energy indexes serve as hedging assets, while S&P TSX provides coverage against specific risks. The Solactive China Clean Energy index, however, may not be suitable for hedging due to strong peer connectivity and a lack of long-term stability. In conclusion, effective alignment of sustainability goals with clean energy investment strategies requires careful consideration of asset characteristics and relationships, emphasizing diversification, and ongoing awareness of evolving dynamics for socially and environmentally responsible investors.

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LIMEN Conference
9th International Scientific-Business Conference – LIMEN 2023 – Leadership, Innovation, Management and Economics: Integrated Politics of Research – CONFERENCE PROCEEDINGS, Hybrid (Graz University of Technology, Graz, Austria), December 7, 2023
LIMEN Conference Proceedings published by the Association of Economists and Managers of the Balkans, Belgrade, Serbia
LIMEN Conference 2023 Conference Proceedings: ISBN 978-86-80194-78-3, ISSN 2683-6149, DOI: https://doi.org/10.31410/LIMEN.2023
Creative Commons NonΒ 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.Β
Suggested citation
Dias, R., Chambino, M., Alexandre, P., & GalvΓ£o, R. (2023). From Crisis to Connectivity: Unraveling Sustainable Energy Indexes. In V. Bevanda (Ed.), International Scientific-Business Conference – LIMEN 2023: Vol 9. Conference Proceedings (pp. 185-191). Association of Economists and Managers of the Balkans. https://doi.org/10.31410/LIMEN.2023.185
References
Attarzadeh, A., & Balcilar, M. (2022). On the dynamic return and volatility connectedness of crypΒtocurrency, crude oil, clean energy, and stock markets: a time-varying analysis. Environmental Science and Pollution Research, 29(43). https://doi.org/10.1007/s11356-022-20115-2Β
Dias, R., Alexandre, P., Teixeira, N., & Chambino, M. (2023). Clean Energy Stocks: Resilient Safe Havens in the Volatility of Dirty Cryptocurrencies. Energies, 16(13). https://doi.org/10.3390/en16135232Β Β
Dias, R., Horta, N., & Chambino, M. (2023). Clean Energy Action Index Efficiency: An Analysis in Global Uncertainty Contexts. Energies, 16(9). https://doi.org/10.3390/en16093937Β
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. EnΒergies, 16(13), 4982. https://doi.org/10.3390/en16134982Β
Gregory, A. W., & Hansen, B. E. (1996). Residual-based tests for cointegration in models with regime shifts. Journal of Econometrics, 70(1), 99β126. https://doi.org/10.1016/0304-4076(69)41685-7Β
Iuga, I. C., Mudakkar, S. R., & Dragolea, L. L. (2023). Time of COVID-19: stability analysis of stocks, exchange rates, minerals and metals markets. Economic Research-Ekonomska IsΒtrazivanja , 36(1). https://doi.org/10.1080/1331677X.2022.2090403Β
Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and seΒrial independence of regression residuals. Economics Letters, 6(3), 255β259. https://doi.org/10.1016/0165-1765(80)90024-5Β Β
Karim, S., Lucey, B. M., Naeem, M. A., & Yarovaya, L. (2023). Extreme risk dependence between green bonds and financial markets. European Financial Management. https://doi.org/10.1111/eufm.12458Β
Levin, A., Lin, C. F., & Chu, C. S. J. (2002). Unit root tests in panel data: Asymptotic and finite-samΒple properties. Journal of Econometrics. https://doi.org/10.1016/S0304-4076(01)00098-7Β
Pham, L., & Nguyen, C. P. (2021). Asymmetric tail dependence between green bonds and other asΒset classes. Global Finance Journal, 50. https://doi.org/10.1016/j.gfj.2021.100669Β
Urom, C. (2023). Timeβfrequency dependence and connectedness between financial technology and green assets. International Economics, 175. https://doi.org/10.1016/j.inteco.2023.06.004Β
Wang, P., Zhang, H., Yang, C., & Guo, Y. (2021). Time and frequency dynamics of connectedness and hedging performance in global stock markets: Bitcoin versus conventional hedges. ReΒsearch in International Business and Finance, 58. https://doi.org/10.1016/j.ribaf.2021.101479Β


