Dias, R.; Horta, N.; Chambino, M. Clean Energy Action Index Efficiency: An Analysis in Global Uncertainty Contexts. Energies2023, 16, 3937.
Dias, R.; Horta, N.; Chambino, M. Clean Energy Action Index Efficiency: An Analysis in Global Uncertainty Contexts. Energies 2023, 16, 3937.
Dias, R.; Horta, N.; Chambino, M. Clean Energy Action Index Efficiency: An Analysis in Global Uncertainty Contexts. Energies2023, 16, 3937.
Dias, R.; Horta, N.; Chambino, M. Clean Energy Action Index Efficiency: An Analysis in Global Uncertainty Contexts. Energies 2023, 16, 3937.
Abstract
Climate change, the scarcity of fossil fuels, technical advances in clean energy, and the volatility of crude oil prices are just a few of the factors that have prompted the world to recognize clean energy as a viable alternative to dirty energy. As part of the Paris Climate Accord of 2015, many countries agreed to change their economies to be more sensitive to climate change. Due to this Accord, which increased interest from investors and decision-makers, investments in clean energy companies have benefited [1,2]. Clean energy stocks, which are a part of the larger world of tradable reserves, might experience pricing inefficiencies. In this paper, we investigate the multifractal scaling behavior and efficiency of green finance markets, as well as traditional markets like gold, crude oil, and natural gas between January 1, 2018, and March 9, 2023, which covers periods of low volatility and financial instability (2020 and 2022 events). To test the serial dependency (autocorrelation) and the efficient market hypothesis, in its weak form, we employed the Lo and Mackinlay test and the DFA method. The empirical findings demonstrated that both periods exhibit severe multifractal and significant asymmetry, indicating that the price indices under study are not at all efficient.
Keywords
clean energy; correlation; market efficiency
Subject
Business, Economics and Management, Business and Management
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
