Abstract
Renewable energy tokens allow investors to diversify their portfolios while supporting renewable energy projects. While renewable energy tokens and renewable energy stocks are different investment instruments with different characteristics, there can be important links between them, as both offer the opportunity to invest in renewable energy sources. The aim of the study is to investigate the return connectedness between renewable energy tokens and renewable energy stock indices. EWT, PWR, SNC, and WPR are used to represent renewable energy tokens, and WilderHill Clean Energy Index, S&P Global Clean Energy Index, and European Renewable Energy Index indices are used to represent renewable energy stock markets. In the study, return connectedness between assets is investigated with the QVAR model. The return spillovers between renewable energy tokens and renewable energy stock indices show that they vary under different market conditions. Under normal market conditions, renewable energy tokens and renewable energy indices are significantly unconnected. During extreme market downturns and upturns, the return interconnectedness among these assets significantly increases. Moreover, the return spillovers between renewable energy tokens and renewable energy stock indices are asymmetric and time-varying. The return connectedness between these assets is affected by extreme events such as COVID-19, the Russia-Ukraine war, and the collapse of the cryptocurrency market. Since the net return spillover relationships between assets are time-varying and under different market conditions, investors and portfolio managers should constantly review the net return spillovers between assets and adjust their positions accordingly.
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Notes
The most popular of these are Energy Web Token, Powerledger, SunContract, and WePower.
March 3, 2020, is the first trading date of EWT, which has the highest market cap.
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Ustaoglu, E. Extreme return connectedness between renewable energy tokens and renewable energy stock markets: evidence from a quantile-based analysis. Environ Sci Pollut Res 31, 5086–5099 (2024). https://doi.org/10.1007/s11356-023-31563-9
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DOI: https://doi.org/10.1007/s11356-023-31563-9