Open Access Letter

Asymmetric Efficiency: Contrasting Sustainable Energy Indices with Dirty Cryptocurrencies

by Rosa Galvão a,*  and  Rui Dias a, b
a
Accounting and Finance Department, Instituto Politécnico de Setúbal, Setúbal, Portugal
b
Center for Studies and Advanced Training in Management and Economics (CEFAGE), University of Évora, Évora, Portugal
*
Author to whom correspondence should be addressed.
FEL  2024, 22; 3(1), 22; https://doi.org/10.58567/fel03010002
Received: 20 November 2023 / Accepted: 3 December 2023 / Published Online: 16 January 2024
View Full-Text
Download PDF

Abstract

This paper examines the efficiency, in its weak form, of the clean energy stock indices, Clean Coal Technologies, Clean Energy Fuels, and Wilderhill, as well as the cryptocurrencies classified as "dirty", due to their excessive energy consumption, such as Bitcoin (BTC), Ethereum (ETH), Ethereum Classic (ETH Classic), and Litecoin (LTC), from January 2020 to May 30, 2023. In order to meet the research objectives, the aim is to answer the following research question, namely whether: i) the events of 2020 and 2022 accentuated the persistence in the clean energy and dirty energy indices? The results show that clean energy indices such as digital currencies classified as "dirty" show autocorrelation in their returns; the prices are not independent and identically distributed (i.i.d). In conclusion, arbitrage strategies can be used to obtain abnormal returns, but caution is needed as prices can rise above their real market value and reduce trading profitability. This study contributes to the knowledge base on sustainable finance by teaching investors how to use forecasting strategies on the future values of their investments.

Keywords: Clean energy; Cryptocurrencies; Persistence of returns; Autocorrelation;
Copyright: © 2024 by Galvão and Dias. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Share and Cite

ACS Style
Galvão, R.; Dias, R. Asymmetric Efficiency: Contrasting Sustainable Energy Indices with Dirty Cryptocurrencies. Financial Economics Letters, 2024, 3, 22. https://doi.org/10.58567/fel03010002
AMA Style
Galvão R, Dias R. Asymmetric Efficiency: Contrasting Sustainable Energy Indices with Dirty Cryptocurrencies. Financial Economics Letters; 2024, 3(1):22. https://doi.org/10.58567/fel03010002
Chicago/Turabian Style
Galvão, Rosa; Dias, Rui 2024. "Asymmetric Efficiency: Contrasting Sustainable Energy Indices with Dirty Cryptocurrencies" Financial Economics Letters 3, no.1:22. https://doi.org/10.58567/fel03010002
APA style
Galvão, R., & Dias, R. (2024). Asymmetric Efficiency: Contrasting Sustainable Energy Indices with Dirty Cryptocurrencies. Financial Economics Letters, 3(1), 22. https://doi.org/10.58567/fel03010002

Article Metrics

Article Access Statistics

References

  1. Breitung, J. (2000). The local power of some unit root tests for panel data. Advances in Econometrics. https://doi.org/10.1016/S0731-9053(00)15006-6
  2. Chambino, M., Teixeira Dias, R. M., & Rebolo Horta, N. (2023). Asymmetric efficiency of cryptocurrencies during the 2020 and 2022 events. Economic Analysis Letters. https://doi.org/10.58567/eal02020004
  3. Dias, R., Chambino, M., & Horta, N. H. (2023). Long-Term Dependencies in Central European Stock Markets : A Crisp-Set. Economic Analysis Letters 2(1), 10–17. https://doi.org/10.58567/eal02010002
  4. 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/10.3390/en16093937
  5. Dias, R., Horta, N., Chambino, M., Alexandre, P., & Heliodoro, P. (2022). A Multiple Fluctuations and Detrending Analysis of Financial Market Efficiency: Comparison of Central and Eastern European Stock Indexes. International Scientific-Business Conference-LIMEN 2022: Vol 8. Conference Proceedings, 11–21. https://doi.org/10.31410/limen.2022.11
  6. Dias, R. M., Teixeira, N., Pardal, P., & Godinho, T. (2023). Volatility Transmission Between ASEAN-5 Stock Exchanges. International Journal of Corporate Finance and Accounting 10(1), 1–17. https://doi.org/10.4018/ijcfa.319711
  7. Dias, R., Pereira, J. M., & Carvalho, L. C. (2022). Are African Stock Markets Efficient? A Comparative Analysis Between Six African Markets, the UK, Japan and the USA in the Period of the Pandemic. Naše Gospodarstvo/Our Economy 68(1), 35–51. https://doi.org/10.2478/ngoe-2022-0004
  8. 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
  9. 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 markets within the context of the global Covid-19 pandemic. Oeconomia Copernicana 11(4). https://doi.org/10.24136/OC.2020.024
  10. 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
  11. Fama, E. F. (1965). Random Walks in Stock Market Prices. Financial Analysts Journal. https://doi.org/10.2469/faj.v21.n5.55
  12. Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance. https://doi.org/10.2307/2325486
  13. Fama, E. F. (1991). Efficient Capital Markets: II. The Journal of Finance. https://doi.org/10.2307/2328565
  14. Fuentes, F., & Herrera, R. (2020). Dynamics of connectedness in clean energy stocks. Energies 13(14). https://doi.org/10.3390/en13143705
  15. Horta, N., Dias, R., & Chambino, M. (2022). Efficiency and Long-Term Correlation in Central and Eastern European Stock Indexes: An Approach in the Context of Extreme Events in 2020 and 2022. International Scientific-Business Conference-LIMEN 2022: Vol 8. Conference Proceedings, 23–37. https://doi.org/10.31410/limen.2022.23
  16. 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
  17. Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters 6(3), 255–259. https://doi.org/10.1016/0165-1765(80)90024-5
  18. Levin, A., Lin, C. F., & Chu, C. S. J. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics. https://doi.org/10.1016/S0304-4076(01)00098-7
  19. Lo, A. W., & MacKinlay, A. C. (1988). Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test. Review of Financial Studies. https://doi.org/10.1093/rfs/1.1.41
  20. 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
  21. Rosenthal, L. (1983). An empirical test of the efficiency of the ADR market. Journal of Banking & Finance 7(1), 17–29. https://doi.org/10.1016/0378-4266(83)90053-5
  22. 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), 1–12. https://doi.org/10.3390/su15053945
  23. Shahzad, S. J. H., Bouri, E., Kayani, G. M., Nasir, R. M., & Kristoufek, L. (2020). Are clean energy stocks efficient? Asymmetric multifractal scaling behaviour. Physica A: Statistical Mechanics and Its Applications, 550. https://doi.org/10.1016/j.physa.2020.124519
  24. Teixeira Dias, R. M., Horta, N. R., & Chambino, M. (2023). Portfolio rebalancing in times of stress: Capital markets vs. Commodities. Journal of Economic Analysis, 2(February), 63–76. https://doi.org/10.58567/jea02010005
  25. Teixeira, N., Dias, R. T., Pardal, P., & Horta, N. R. (2022). Financial Integration and Comovements Between Capital Markets and Oil Markets. In I. Lisboa, N. Teixeira, L. Segura, T. Krulický, & V. Machová (Eds.), Handbook of Research on Acceleration Programs for SMEs (Issue December, pp. 240–261). IGI Global. https://doi.org/10.4018/978-1-6684-5666-8.ch013
  26. Thai, H. N. (2021). Quantile dependence between green bonds, stocks, bitcoin, commodities and clean energy. Economic Computation and Economic Cybernetics Studies and Research 55(3). https://doi.org/10.24818/18423264/55.3.21.05
  27. Uddin, G. S., Rahman, M. L., Hedström, A., & Ahmed, A. (2019). Cross-quantilogram-based correlation and dependence between renewable energy stock and other asset classes. Energy Economics 80, 743–759. https://doi.org/10.1016/J.ENECO.2019.02.014
  28. Wan, D., Xue, R., Linnenluecke, M., Tian, J., & Shan, Y. (2021). The impact of investor attention during COVID-19 on investment in clean energy versus fossil fuel firms. Finance Research Letters. https://doi.org/10.1016/j.frl.2021.101955
  29. Yao, C. Z., Mo, Y. N., & Zhang, Z. K. (2021). A study of the efficiency of the Chinese clean energy stock market and its correlation with the crude oil market based on an asymmetric multifractal scaling behavior analysis. North American Journal of Economics and Finance 58. https://doi.org/10.1016/j.najef.2021.101520