Clean Energy Stocks: Resilient Safe Havens in the Volatility of Dirty Cryptocurrencies [Circular Business Models and Circular Economy in Energy Production and Consumption]( /journal/energies/special_issues/V3PK1OF3WI )) Abstract: [cryptocurrencies](/search?q=cryptocurrencies); [clean energy](/search?q=clean+energy); [safe haven](/search?q=safe+haven); [spillover](/search?q=spillover) 1.Introduction [1](#B1-energies-16-05232)].[2](#B2-energies-16-05232)] conducted a study that emphasized that the rising adoption of Bitcoin, the leading “dirty” cryptocurrency, could trigger carbon emissions that might cause a rise in global temperature by two degrees Celsius within a span of thirty years.At present, the energy consumption attributed to Bitcoin has been estimated to be 169.98 TWh per year, surpassing the gross annual energy consumption of Poland.The substantial consumption of energy can be attributed to the computationally intensive Proof-of-Work (PoW) system employed by Bitcoin.It is noteworthy that a single Bitcoin transaction has the potential to use approximately 1834.02 kWh of electrical energy, a quantity in line with the energy consumption of a typical American family over a period of approximately 62 days.Several researchers, such as in [ [3](#B3-energies-16-05232)], have highlighted the urgent need to curtail cryptocurrency mining activities and encourage the adoption of non-PoW cryptocurrencies.
The aforementioned trend is driven by rising concerns regarding the ecological implications of energy-intensive digital currencies.As a reaction to these concerns, a growing number of environmentally conscious digital currencies, commonly referred to as “clean” cryptocurrencies, have surfaced in the marketplace.The present shift towards a more sustainable industry has led to a growing appreciation and valuation of green cryptocurrencies.
It is worth noting that specific clean cryptocurrencies, such as Cardano and Solana, have already attained substantial market capitalization and positioned themselves as leading players.Simultaneously, there is a notable upward trajectory in the clean energy industries.Clean energy companies generated revenue approaching USD 700 billion, along with an annual growth rate of 6.8%.This suggests a favorable trend and increasing significance related to clean energy within the industry.[4](#B4-energies-16-05232)] conducted a study to investigate the interdependence of information among major cryptocurrencies and different commodities.
The authors emphasize that cryptocurrencies, specifically Bitcoin, remain incorporated within energy markets, including but not limited to natural gas, heating oil, and crude oil.
Furthermore, the authors of [ [5](#B5-energies-16-05232)] have shown that the financial correlation between Bitcoin and traditional assets such as stocks, oil, and gold has exhibited a weak association, though it is gradually bolstering.The study conducted in [ [6](#B6-energies-16-05232)] aimed to examine the lead–lag relationships between Bitcoin and energy commodities, specifically crude oil, natural gas, and coal.The authors’ findings revealed the existence of lead–lag associations between Bitcoin and crude oil as well as natural gas, while coal did not exhibit such relationships.The present scenario is of interest, given that China, recognized as the foremost Bitcoin mining jurisdiction, is significantly reliant on coal as a source of energy production.
The study in [ [7](#B7-energies-16-05232)] delved into the intricate relationship of dynamic correlation and extreme dependence that exists between the Bitcoin and Chinese coal markets.The researcher’s findings show that there is a growing correlation between Bitcoin and coal indexes during periods of extreme mining activities in China, which has a notable effect on the price of Bitcoin.Several studies, such as those carried out in [ [8](#B8-energies-16-05232), [9](#B9-energies-16-05232)], have investigated the potential interplay of side effects between Bitcoin and other markets.The study conducted in [ [10](#B10-energies-16-05232)] revealed the existence of both bidirectional and unidirectional spillover effects between the crude oil market and cryptocurrencies.
The findings suggest that crude oil can potentially serve as a safe haven from the risks associated with different types of cryptocurrencies.The authors of [ [1](#B1-energies-16-05232), [11](#B11-energies-16-05232)] have identified noteworthy correlations and volatility correlations between major cryptocurrencies and electricity markets, underscoring the interconnection of digital currency and the energy industry.The findings of [ [12](#B12-energies-16-05232)] suggest that the cryptocurrency market exhibits a lower degree of connection with the global technology industry, thereby implying a unique association between cryptocurrencies and industries with a technology-oriented focus.According to the findings of [ [13](#B13-energies-16-05232)], the global pandemic in 2020 caused a major impact on the markets under examination, leading to an increase in volatility.The authors of the study highlight that among different assets, only gold and the U.S.dollar are regarded as safe havens, while assets such as Bitcoin, oil, and technology shares are considered major recipients and do not qualify as safe havens.[14](#B14-energies-16-05232)] have identified notable spillover effects of returns from the energy and technology markets onto Bitcoin.Additionally, they have observed volatility spillovers in Bitcoin from the long-term energy markets and from the short-term technology market to Bitcoin.
While the authors of [ [1](#B1-energies-16-05232)] have demonstrated that there is no significant link between Bitcoin price volatility and the most dominant green ETF markets.According to the study in [ [15](#B15-energies-16-05232)], there is no dependence between clean energy and Bitcoin.However, they suggest that clean energy might act as a means of diversification for Bitcoin, as it offers a higher coverage ratio and, therefore, a more limited exposure to risk when held in the wallet.The authors of [ [16](#B16-energies-16-05232)] posit that green investments may provide diversification benefits for cryptocurrency, a notion that is congruent with prior research.
The authors have drawn attention to a tenuous link between cryptocurrencies such as Bitcoin and Ethereum and green assets during non-crisis periods.The aforementioned documents have prompted a question about the potential of clean energy markets to act as a safe haven for Bitcoin, Ethereum, and other cryptocurrencies.The identification of a potential correlation between specific categories of clean energy stocks and certain types of cryptocurrencies, whereby they may serve as a mutually beneficial safe haven, holds significant implications for investors.An investor may find it pragmatic to secure themselves against an eventual drop in cryptocurrency prices by investing in clean energy stocks, or conversely, to protect themselves against a potential downturn in clean energy stocks by investing in cryptocurrencies, knowing that the type of the cryptocurrency holds relevance.The observation that only dirty cryptocurrencies act as a safe haven against clean energy implies that an economic incentive for pouring resources into clean energy will run counter to the ecological argument.Despite the considerable efforts invested in interconnecting cryptocurrencies with other financial assets, the discussion surrounding the degree of isolation of the Bitcoin or cryptocurrency market from other assets (markets) remains unfinished.
[17](#B17-energies-16-05232)] to understand the level of integration during the two subperiods (Tranquil and Stress).To conduct a robustness check on the prior findings, we will employ non-conditional correlations to estimate the model in [ [18](#B18-energies-16-05232)].The purpose is to determine whether the eventual increase in correlation resulting from the events of 2020 and 2022 leads to volatility spillovers between clean energy indexes and cryptocurrencies categorized as “dirty” due to their energy-intensive mining and transaction procedures.
[Section 2](#sec2-energies-16-05232)of the manuscript presents a thorough examination of the current body of literature.[Section 3](#sec3-energies-16-05232)describes the data and methodology used for the analysis.[Section 4](#sec4-energies-16-05232)of this paper outlines the empirical results, whereas [Section 5](#sec5-energies-16-05232)presents a comprehensive analysis of the study’s implications.
Finally, [Section 6](#sec6-energies-16-05232)provides a conclusion.2.Literature Review [19](#B19-energies-16-05232), [20](#B20-energies-16-05232)], revealing their superiority over traditional stock and securities indexes.The study conducted in [ [21](#B21-energies-16-05232)] provides a more comprehensive outlook, in contrast to prior research that mainly examined the relationship between cryptocurrencies and traditional energy assets.The present research investigates the function of diverse assets, such as Bitcoin, gold, stocks, currencies, and energy commodities (namely, oil and natural gas), within the global network of volatility interconnection.The authors highlight the noteworthy influence of external investors’ attention on the expansion of volatility within financial markets.The authors of [ [21](#B21-energies-16-05232)] have contributed to the understanding of the dynamic nature of asset interconnections through their research findings.The analysis reveals that Bitcoin, gold, exchanges, and natural gas are identified as transmitters of volatility, thereby indicating their influence on the transmission of market volatility.
In contrast, crude oil and stock markets serve as indicators of vulnerability to external shocks and fluctuations.[22](#B22-energies-16-05232)], holds significant importance for investors.This is because it enables them to evaluate the environmental impact of their investments, appraise financial performance, absorb policies and regulatory scenarios, and manage the energy transition.Clean energy stock indexes include companies engaged in sustainable technologies and renewable energy sources.
The dirty energy stock indexes represent corporations involved in the extraction and use of fossil fuels, which have been identified as significant contributors to environmental deterioration.The adoption of trade-off analysis allows investors to efficiently match their portfolios with sustainability objectives, make well-informed financial decisions, anticipate regulatory adjustments, and take advantage of emerging opportunities in developing energy markets.This introduction sets the foundation for a more comprehensive investigation into the intricacies of these particular categories of assets and the potential implications for investors within the ever-changing financial and sustainable energy markets.[23](#B23-energies-16-05232)], the decrease in oil discoveries observed in recent decades has led to the recognition of the crucial role that sustainable energy production technologies are expected to play in addressing future energy demand.
According to [ [24](#B24-energies-16-05232)], efficiency and energy management play a significant role in driving human progress.Therefore, the scientific community holds a significant interest in energy trends.According to [ [25](#B25-energies-16-05232)], the management of electricity consumption costs continues to be a significant issue of interest for environmental promoters.The study conducted in [ [26](#B26-energies-16-05232)] aimed to investigate the potential impact of clean energy investments on the risk profile of investors.In pursuit of this objective, the researchers put forth a dynamic approach to conducting a comparative risk assessment of three portfolios characterized by minimal variance.These portfolios include one exclusively comprised of dirty energy assets, another exclusively comprised of clean energy assets, and a combined portfolio that incorporates both types of energy.The findings indicate that, in contrast to previous economic crises such as the subprime mortgage meltdown and Brexit, there has been a notable shift towards favoring investment in clean energy over fossil fuels in the wake of the pandemic crisis.This preference is driven not only by considerations of profitability but also by perceptions of lower risk associated with clean energy investments.
Furthermore, the authors of [ [27](#B27-energies-16-05232)] conducted an analysis covering the period from 19 January 2010 to 4 April 2022, revealing that investment in clean energy companies is currently advantageous not only due to its role in facilitating a sustainable transition to renewable energy sources but also due to its financial appeal.This fact presents a promising prospect amidst the environmental emergency and serves as a potential solution to mitigate the prevailing geopolitical tensions arising primarily from the energy reliance of certain nations, as their energy composition remains predominantly reliant on fossil fuels.Therefore, the allocation of investments towards clean energy companies that demonstrate alignment with socially responsible goals may provide both financial and environmental benefits.
This is due to the expansion of markets, governmental incentives, and the increasing demand for sustainable solutions.2.1.Studies Related to Research on Safe Haven Properties of Clean Energy Indexes and Cryptocurrencies [28](#B28-energies-16-05232), [29](#B29-energies-16-05232), [30](#B30-energies-16-05232)].The authors of [ [28](#B28-energies-16-05232)] proposed that an upsurge in the prices of traditional energy sources and the implementation of carbon pricing would encourage investments in clean energy firms.The study revealed that the stock prices of clean energy firms were subject to the impact of both oil prices and technology stock prices, thereby casting aspersions on the effectiveness of hedge and safe haven effects.
Concurrently, the authors of [ [29](#B29-energies-16-05232)] conducted a study to examine the relationships between oil prices, clean energy stock prices, and technology stock prices.The researchers’ discoveries revealed a structural change during the latter part of 2007, which corresponded with a notable escalation in the cost of oil.The authors’ research revealed a positive correlation between oil prices and clean energy prices subsequent to structural breaks, which contradicts previous studies and questions the impact of safe haven effects on portfolio diversification.The authors of [ [30](#B30-energies-16-05232)] conducted an analysis on the implications of shocks on safe haven properties and diversification of clean energy portfolios, specifically with regard to the WilderHill New Energy Global Innovation Index (NEX), technology shares (PSE), four energy subindexes of the Standard & Poor’s Goldman Sachs Commodity Index (S&P-GSCI), three leading global stock indexes represented by the U.S.
and Europe, and the Dow Jones Islamic Market Index (DJIMI), as well as the USD-Euro exchange rate.The study conducted by the authors suggests that the addition of NEX to the energy portfolio results in improved diversification and risk mitigation advantages owing to the safe haven properties that it offers portfolio managers.[31](#B31-energies-16-05232), [32](#B32-energies-16-05232), [33](#B33-energies-16-05232)].The study conducted in [ [31](#B31-energies-16-05232)] was designed to examine the safe haven capacity of clean and green assets in relation to two dirty energy assets, namely disguised crude oil prices and energy ETFs.The research used daily data that extended from 3 January 2012 to 29 November 2019.The researcher’s results provided evidence backing the idea of implementing a dynamic hedge strategy and suggested that clean energy initiatives were a more efficient hedge than green bonds, particularly in the context of crude oil.
Similarly, the authors of [ [32](#B32-energies-16-05232)] conducted a study of the dynamic dependence structure between green bonds (UKs) and different global clean energy (CE) markets within the period of 5 July 2011–24 February 2020.The research findings indicate a significant dependence between the stock markets of the UK and CE.Furthermore, the authors have noted the occurrence of bidirectional shocks resulting from the occurrence of extreme low or high movements in the CE stock market.This observation implies that investors from the UK have successfully allocated their capital towards economic activities that produce low carbon emissions.The study conducted in [ [33](#B33-energies-16-05232)] examined the safe haven characteristics of clean energy indexes in relationship with two distinct types of cryptocurrencies, namely black, or “dirty”, and green, or “clean”, based on their energy consumption levels.
The statistical analysis conducted indicated that clean energy failed to provide direct protection for any type of cryptocurrency.Nevertheless, it worked as a weak safe haven for both parties during periods of significant market downturns.The research indicates that during periods of heightened uncertainty, clean energy tended to act as a safer haven for cryptocurrencies with a higher carbon footprint (“dirty crypto”) as opposed to those with a lower carbon footprint (“clean crypto”).
[15](#B15-energies-16-05232), [16](#B16-energies-16-05232), [34](#B34-energies-16-05232)] to investigate the extent of dependence between clean and green assets and cryptocurrencies.The primary objective of these studies was to ascertain whether clean assets exhibit safe-haven properties during times of market uncertainty on a global level.The authors of [ [15](#B15-energies-16-05232)] highlighted the existence of multiple dependence situations between bitcoin and green financial assets.The dependence structure was found to be mainly asymmetric and subject to shifting as time went by.
Furthermore, the author’s review of the efficiency of using bitcoin as a safe haven for green financial assets suggested that all clean energy green assets were effective in acting as safe havens against bitcoin.The authors of [ [34](#B34-energies-16-05232)] conducted a study that intended to examine the relationship between cryptocurrencies, green bonds, and other assets in terms of time and frequency.
The findings of the study revealed significant relationships between markets, which cast doubt on the hypothesis of safe haven assets.Nevertheless, the main emphasis was on technology rather than clean energy indexes.The study conducted in [ [16](#B16-energies-16-05232)] used a TVP-VAR network connectivity model to examine the impact of variable-time shocks on investments in cryptocurrencies, green assets, and fossil fuels.The study revealed that the shocks between cryptocurrencies, green assets, and fossil fuels showed temporal fluctuations and exhibited higher levels during periods of crisis.
[33](#B33-energies-16-05232), [35](#B35-energies-16-05232), [36](#B36-energies-16-05232)].The present study examined the potential of clean energy stock indexes to function as protective assets or safe havens in the context of dirty assets.The authors of [ [35](#B35-energies-16-05232)] conducted an investigation into the dependence of clean energy markets on dirty assets, namely oil and Bitcoin, during a period lasting from 2011 to 2019.The authors show a notable degree of integration in terms of spillover effects, lagged returns, risks, and extreme events that affect both clean energy markets and oil prices.
The researchers noted that there were both symmetrical and asymmetrical effects between returns and risks, contingent upon the prevailing market circumstances, specifically in relation to downturn and upturn movements.The impact of oil spillover effects on the clean energy market was observed prior to the Paris Agreement; however, no evidence was found after.Additionally, the present analysis highlights the dependence between clean energy and Bitcoin, revealing a significant spillover effect from rare events, implying a potential substitution effect.The authors of [ [33](#B33-energies-16-05232)] conducted an analysis of the hedge and safe haven characteristics of several clean energy indexes in relation to two distinct categories of cryptocurrencies, classified based on their energy consumption levels as either “dirty” or “clean”.The findings suggest that the utilization of clean energy sources does not provide direct protection for any type of digital currency.Nevertheless, it functioned as a suboptimal refuge for both parties amidst market conditions.In addition, it is probable that clean energy will act as a safe haven for dirty cryptocurrencies rather than clean currencies in times of heightened uncertainty.The study conducted in [ [36](#B36-energies-16-05232)] studied the dependency between clean energy, green markets, and cryptocurrencies during the period that went from January 2018 to November 2021.
The study revealed that sustainable investments, as exemplified by the DJSI and ESGL indexes, had a significant impact on the network system during the COVID-19 pandemic.The authors pointed out that green bonds exhibit a reduced degree of integration with other financial markets, suggesting their ability to provide investors with diversification benefits.[37](#B37-energies-16-05232), [38](#B38-energies-16-05232)] carried out research on the hedging and safe haven attributes of clean energy stock indexes with respect to distinct asset classes.The study conducted in [ [37](#B37-energies-16-05232)] aimed to investigate the correlations and relationships between green economy indexes, dirty cryptocurrencies, and clean cryptocurrencies in the markets of the U.S., Europe, and Asia over the period that extends from 9 November 2017 to 4 April 2022.The study’s empirical results indicate that there is an overall link between green economy indexes and clean cryptocurrencies in comparison to dirty cryptocurrencies.Clean cryptocurrencies gained prominence in the year 2020, which was characterized by the onset of the COVID-19 pandemic.
The research findings have revealed a noteworthy spillover effect across the three Asian markets, thereby casting uncertainty on the efficiency of hedge and safe haven characteristics.The study conducted in [ [38](#B38-energies-16-05232)] examined the co-movements in the clean and dirty energy stock indexes before and during the global pandemic of the COVID-19 in 2020.
The findings suggest that there exist weak links between clean energy markets and those related to dirty energy, in both the short and long term.It is noteworthy that a clear dissociation condition was observed between the two energy markets.Additionally, the research showed that the clean energy markets remained relatively insulated from the impacts of the pandemic-induced economic downturn, underscoring the advantages of diversifying investments across both clean and dirty energy markets.2.2.
Driving Change: China’s Green Tax Policy and Environmental Transformation [39](#B39-energies-16-05232), [40](#B40-energies-16-05232), [41](#B41-energies-16-05232), [42](#B42-energies-16-05232), [43](#B43-energies-16-05232)].
3.Materials and Methods 3.1.Materials [Table 1](#energies-16-05232-t001), included the WilderHill Clean Energy Index (ECO), Nasdaq OMX Green Economy (QGREEN), and Clean Energy Fuel (CLNE).
Green economy stock indexes are designed to track the performance of enterprises that operate in environmentally friendly or sustainable industries.These industries typically prioritize environmental sustainability and involve renewable energy, clean technology, energy efficiency, sustainable agriculture, waste management, and related fields.The Green Economy stock indexes try to provide investors with an opportunity to invest in firms that prioritize sustainability and are poised to expand as the global community transitions towards a more ecologically conscious and sustainable future.On the other hand, the digital currencies used for the research include Bitcoin (BTC), Ethereum (ETH), and Ethereum Classic (ETC).
The cryptocurrencies in question operate on the Proof-of-Work (PoW) protocol, whereby miners are tasked with solving complex mathematical challenges in order to validate transactions and append new blocks to the blockchain.Nevertheless, Proof-of-Work (PoW)-based cryptocurrencies have been subject to criticism due to their substantial energy consumption during the mining and verification of transactions.In order to boost the robustness of the results, the sample was divided into two distinct subperiods.Specifically, the period from 16 May 2018 to 31 December 2019, was labeled as “Tranquil”, while the period from 1 January 2020 to 15 May 2023, was titled as “Stress”.
This partitioning was done to account for the events that occurred in 2020 and 2022.
[44](#B44-energies-16-05232)] propose using a series of returns instead of a price series to examine financial market behavior, as investors are primarily concerned with determining the returns of an asset or portfolio of assets.Complementarity is characterized by the statistical properties of the return series, which facilitate analytical treatment due to the presence of stationarity, a feature typically absents in the price series.3.2.
Methods [45](#B45-energies-16-05232)], which assumes the normality of the data.To ascertain the stationarity assumption of the time series, we will employ the panel’s unit root test [ [46](#B46-energies-16-05232)] and the unit panel tests [ [47](#B47-energies-16-05232)]—Fisher’s Chi-square and Choi Z-stat.
The PP test, which is also referred to as the Pesaran and Pesaran test, uses Fisher’s chi-square statistics to assess cross-dependency between panel time series.The PP-Choi Z-stat test, as proposed in [ [48](#B48-energies-16-05232)], is a statistical method that examines the existence of cross-dependence in panel data.
This test employs Z statistics to ascertain the presence of correlation or interdependence among the observations of time series in the panel.The unit root test developed in [ [49](#B49-energies-16-05232)] will be employed to determine the most prominent structural break and its corresponding year.A structural break denotes a substantial modification in the level and/or trend of a time series, which may have either a permanent or temporary nature.In the circumstance that the series is considered stationary, it follows that any shocks experienced must only have temporary effects, as any permanent effects would be precluded.
The assessment of structural breaks in this investigation is essential for drawing conclusions regarding their consequences and implications, including the year in which they transpire.By detecting whether structural breaks are associated with a particular crisis, it is possible to avoid spurious results, such as the rejection of the null hypothesis of a unitary root, when the series is actually affected by structural breaks.[17](#B17-energies-16-05232)] will be employed to verify the integration or segmentation of clean energy stock indexes and digital currencies by virtue of analyzing a tumultuous era in the global economy.The methodology proposed in [ [17](#B17-energies-16-05232)] exhibits a high degree of robustness in highly volatile financial market conditions.
This is due to the authors’ approach of extending the conventional co-integration tests to account for a potential shift in the co-integration vector at an unknown point in time.
The researchers examined 4 integration models.
The initial model integrates a modification in level, denoted as Level: [50](#B50-energies-16-05232)], is used to assess whether the correlation coefficient matrix is globally significantly different from the identity matrix.The present study aims to investigate the presence of volatility spillovers between clean energy stock indexes and dirty-classified cryptocurrencies.To achieve this objective, the -test of the heteroscedasticity of two samples from [ [18](#B18-energies-16-05232)] will be employed.
The methodology employed in this study posits a null hypothesis wherein the correlation observed during the Stress subperiod is either less than or equal to the correlation observed during the Tranquil subperiod.Conversely, the alternative hypothesis suggests that the correlation during the Stress period is both higher and statistically significant.The economic implications of the null hypothesis rejection are linked to the phenomenon of volatility spillover.
The absence of rejection shows interdependence.Regarding the model, the estimation process comprises the subsequent steps: [51](#B51-energies-16-05232)], which is then applied to the correlation coefficients such that they exhibit, in asymptotic terms, an approximately normal distribution with an average of and a variance of , as follows: 4.Results 4.1.Descriptive Statistics [Figure 1](#energies-16-05232-f001)displays the price index fluctuations for different clean energy stock indexes, namely the WilderHill Clean Energy Index (ECO), Nasdaq OMX Green Economy (QGREEN), and Clean Energy Fuel (CLNE), as well as digital currencies such as Bitcoin (BTC), Ethereum (ETH), and Ethereum Classic (ETC).The observed period spans from 16 May 2018 to 15 May 2023.By means of graphical analysis, it is possible to observe prominent upward and downward trends in growth, which indicate the occurrence of structural breaks.The year 2021 has witnessed noteworthy advancements and occurrences in the realm of cryptocurrencies, which have significantly influenced their market dynamics and general reception.In April 2021, Bitcoin attained a record-breaking price of over USD 60,000, which was attributed to the impact of Ethereum (ETH).
Ethereum Classic (ETC) also experienced a similar trend.[Table 2](#energies-16-05232-t002)displays a concise overview of the main descriptive statistical indicators, measured in daily returns, for the time series pertaining to the stock indexes WilderHill Clean Energy Index (ECO), Nasdaq OMX Green Economy (QGREEN), Clean Energy Fuel (CLNE), and the digital currencies Bitcoin (BTC), Ethereum (ETH), and Ethereum Classic (ETC).The period under consideration extends from 16 May 2018 to 15 May 2023.Upon examination of the statistical summary table, it is evident that the mean returns exhibit a positive trend.Notably, the digital currency ETC (0.073853) is observed to have the highest standard deviation.We can show that we are working with non-Gaussian distributions by looking at the values of different asymmetries of 0.Specifically, negative asymmetries are observed in BTC (−0.781854), QGREEN (−0.840059), and ECO (−0.321265), while positive asymmetries are observed in CLNE (0.624032), ETH (0.175571), and ETC (0.431585).Furthermore, it is noteworthy that kurtosis exhibits distinct values of 3, such as CLNE (16.30657), QGREEN (14.56515), BTC (12.70407), ETC (9.616025), ETH (8.478345), and ECO (7.361650).
The adherence test in [ [45](#B45-energies-16-05232)] was conducted for validation purposes, and it was observed that the null hypothesis was rejected at a significance level of 1%.The anticipated results can be attributed to the existence of “fat tails”, which denote the occurrence of extreme values, as a consequence of the events that went down in 2020 and 2022.[Figure 2](#energies-16-05232-f002)show the returns of different clean energy stock and digital currency indexes during the period spanning from 16 May 2018 to 15 May 2023.The WilderHill Clean Energy Index (ECO), Nasdaq OMX Green Economy (QGREEN), and Clean Energy Fuel (CLNE) are among the stocks that fall under the category of clean energy.The digital currencies that have been presented for consideration are Bitcoin (BTC), Ethereum (ETH), and Ethereum Classic (ETC).
When examining the Q–Q plot charts, it is apparent that the returns of the stock and digital currency indexes exhibit a leptokurtic distribution as well as asymmetry or distortion.
The non-conformity of the data distribution from the 45-degree linear line that represents a normal distribution is apparent.The exact distribution of the time series being examined cannot be ascertained with assurance.
However, it can be inferred that the distribution is approximately normal based on the application of the Central Limit Theorem (CLT).That deduction is corroborated by the presence of a considerable number of observations within the time series.4.2.Diagnostic 4.2.1.Time Series Stationarity [47](#B47-energies-16-05232)]—Fisher Chi-square and Choi Z-stat, as well as the test in [ [46](#B46-energies-16-05232)], to verify the assumption of stationarity for the stock indexes of the WilderHill Clean Energy Index (ECO), Nasdaq OMX Green Economy (QGREEN), Clean Energy Fuel (CLNE), and the digital currencies Bitcoin (BTC), Ethereum (ETH), and Ethereum Classic (ETC).The robustness of the intersection of tests with opposing null hypotheses lies in its ability to gauge the lag level between each time series until balance is attained, characterized by an average of 0 and a variance of 1.The findings show that the time series exhibits unit roots in the estimation of the original price series.To achieve stationarity, a logarithmic transformation was conducted on the first differences.
This transformation facilitated the rejection of the null hypothesis in the test in [ [47](#B47-energies-16-05232)]—Fisher Chi-square and Choi Z-stat.The findings of [ [46](#B46-energies-16-05232)] tests show that the null hypothesis is upheld, thereby confirming the fundamental assumptions necessary for the reliable estimation of econometric models.(See [Table 3](#energies-16-05232-t003)and [Table 4](#energies-16-05232-t004), respectively).4.2.2.
Time Series Structural Breaks [Figure 3](#energies-16-05232-f003)exhibits the unit root tests developed in [ [49](#B49-energies-16-05232)] applied to different financial indexes, which include the WilderHill Clean Energy Index (ECO), Nasdaq OMX Green Economy (QGREEN), Clean Energy Fuel (CLNE), and digital currencies such as Bitcoin (BTC), Ethereum (ETH), and Ethereum Classic (ETC).The results of the tests reveal the presence of structural breaks during the Tranquil subperiod, which opposes the assumption of stability in the international financial markets during the period in question.The ECO stock indexes exhibit a structural break on 26 December 2018, QGREEN on 24 December 2018, and CLNE on 13 March 2019, which we link to a loss of confidence among green investors in these markets, which is largely attributed to the escalating trade tensions between the United States and China.This has created a sense of uncertainty and apprehension regarding the potential impact on global economic growth.
In 2018, the U.S.Federal Reserve implemented several interest rate hikes as an element of its monetary policy normalization efforts.
The year 2018 experienced a series of events, including the negotiations over Brexit, diplomatic tensions between the United States and North Korea, and regional conflicts, resulting in market instability and volatility.Consequently, several markets experienced a decline in prices.
On 7 December 2017, Bitcoin experienced a significant structural break.After an extended period of interesting price increases, the cryptocurrency underwent a sudden and steep price correction.Bitcoin’s value surged to a not seen peak of over USD 19.000 per unit before experiencing a significant downturn, dropping to approximately USD 13.000 within a short period of time.The mentioned occurrence denoted the conclusion of a notable upward trend and indicated a noteworthy adjustment in the market.
The Ethereum Classic digital currency experienced a structural break on 24 May 2017.
The Ethereum Classic blockchain is the outcome of a disputed hard fork of the initial Ethereum blockchain.Throughout this period, Ethereum Classic went through a significant drop in both its price and overall market capitalization.The drop in price can be linked to an intersection of aspects, which include volatile markets, an uncertain investor outlook, and likely ambiguity concerning the Ethereum Classic network’s future.On 12 June 2017, Ethereum, the cryptocurrency with the second highest market capitalization, experienced a structural break.The Ethereum market has been observing an interesting uptrend, attributed to the growing interest in initial coin offerings (ICOs) and decentralized applications created on blockchain technology.On 12 June, Ethereum experienced a significant decline in value, dropping from its pinnacle of approximately USD 400 to approximately USD 300 within a brief period.The occurrence in question denoted a significant market correction and underscored the inherent volatility of the cryptocurrency market.
[Figure 4](#energies-16-05232-f004)shows the unit root tests of [ [49](#B49-energies-16-05232)] that were applied to the Stress subperiod, enabling the identification of structure breaks.The findings indicate that the main cause of the most noteworthy structural break in these markets is associated with the first and second waves of the COVID-19 global pandemic.On 24 March 2023, the ECO index had the most prominent structural break, while the QGREEN index experienced an identical occurrence on 27 January 2010.The CLNE index also encountered a breakdown on 22 December 2020.Additionally, the BTC and ETH cryptocurrencies experienced the most significant break on 12 March 2020, and the ETC index on 5 May 2021.4.3.Methodological Results [Table 5](#energies-16-05232-t005)shows the results of the integration between the stock indexes WilderHill Clean Energy Index (ECO), Nasdaq OMX Green Economy (QGREEN), Clean Energy Fuel (CLNE), and the digital currencies Bitcoin (BTC), Ethereum (ETH), and Ethereum Classic (ETC) in the Tranquil subperiod.
The findings indicate the presence of five integrations (out of 30 possible).Specifically, the ECO and QGREEN indexes, as well as the digital currencies ETC and ETH, exhibit bidirectional integrations.Additionally, the ETH shows a unidirectional integration with QGREEN.The results indicate that clean energy stock indexes possess safe haven properties in contrast to cryptocurrencies that are commonly referred to as “dirty”.These findings hold significance for investors, as they give them the ability to adjust their portfolios by including assets designated as environmentally friendly.This could potentially facilitate the progression towards a sustainable economy, particularly during a time of stability in the global financial markets.[Table 6](#energies-16-05232-t006)presents the findings of the integration analysis conducted on the stock indexes WilderHill Clean Energy Index (ECO), Nasdaq OMX Green Economy (QGREEN), Clean Energy Fuel (CLNE), and the digital currencies Bitcoin (BTC), Ethereum (ETH), and Ethereum Classic (ETC) during the Stress subperiod.
The results suggest that 15 cases of integration were observed out of a total of 30 possible.The ECO index is distinguished by only having an integration with BTC.
Similarly, the QGREEN index just integrates with ETH.On the other hand, the CLNE index integrates two different digital currencies, namely BTC, ETC, ETH, and one clean energy index, namely ECO, but it does not integrate with QGREEN.Regarding the cryptocurrency BTC, it is observed that it only integrates with the digital currencies ETC and ETH while not being integrated with the clean energy stock indexes, suggesting its safe haven characteristics.The integration of ETC, a digital currency, with other digital currencies as well as clean energy indexes indicates its lack of safe haven properties.On the other hand, ETH is integrated with BTC and ETC but not with clean energy stock indexes, suggesting its possession of safe haven attributes.
The present study’s results partially confirm our research question of whether clean energy stock indexes can act as safe haven assets during times of stress, particularly in the context of events occurring in 2020 and 2022.[50](#B50-energies-16-05232)], is utilized to examine the dissimilarity between the matrix of correlation coefficients and the identity matrix.[Table 7](#energies-16-05232-t007)displays the non-conditional correlation coefficients of statistics for the quiet subperiod.
It is evident that there are nine correlations with significant positive signs.The pairs relating to the ECO-QGREEN indexes exhibit the most substantial positive coefficient (0.7388), while the ETC-ETH digital currencies follow closely behind with a coefficient of 0.6930.When the correlations between clean energy indexes and digital currencies are examined, it becomes clear that the observed values are frequently of low magnitude and may lack statistical significance.[Table 8](#energies-16-05232-t008)shows the non-conditional correlation coefficients of the statistics for the Stress subperiod.It is evident that the correlations have experienced a significant increase, and all the pairs of clean energy stocks and digital currency indexes exhibit a positive correlation.The above observation suggests the potential for bidirectional volatility spillovers between the examined markets.However, it is imperative to verify this evidence through the application of the t-test of heteroscedasticity on two samples sourced from [ [18](#B18-energies-16-05232)] research.5.
Discussion [Table 9](#energies-16-05232-t009)).[Table 10](#energies-16-05232-t010)presents the outcomes of the t-test conducted on the heteroscedasticity of two samples from [ [18](#B18-energies-16-05232)].
The objective of this test was to verify whether the rise in unconditional correlations between digital currencies and stock indexes results in volatility spillover.This spillover effect could potentially compromise the safe haven characteristics of clean energy assets in favor of their “dirty” peers.The findings indicate that the WilderHill Clean Energy Index (ECO) acts as a conduit for volatility to the Clean Energy Fuel Index (CLNE), while the Nasdaq OMX Green Economy (QGREEN) transmits spill-over effects to both CLNE and BTC.The findings suggest that the BTC and ETC digital currencies exhibit volatility spillovers to the CLNE stock index, indicating safe haven characteristics for the ECO and QGREEN indexes, as well as for other cryptocurrencies, during the events that occurred in 2020 and 2022.The digital currency ETH exhibits spillover effects on the QGREEN and CLNE stock indexes as well as BTC, indicating its potential as a safe haven asset for the ECO index and the cryptocurrency ETC.6.
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https://scholar.google.com/scholar_lookup?title=The+Genetical+Theory+of+Natural+Selection&author=Fisher,+R.A.&publication_year=1930] |Indexes and Cryptocurrencies||Purpose| |WilderHill Clean Energy||ECO||The aim of this index is to accurately reflect the performance of U.S.clean energy enterprises.| |Nasdaq OMX Green Ecomony||QGREEN||The present index encompasses enterprises engaged in the manufacturing and dissemination of biofuels and other environmentally friendly fuels.Biofuels are a type of fuel that is obtained from renewable sources, specifically plant biomass.| |Clean Energy Fuels||CLNE||The index denotes the stock prices of corporations which operate in the clean energy markets, with a specific focus on sustainable energy solutions and alternative fuel sources.| |Bitcoin Crypto||BTC||Bitcoin (BTC) is a form of digital currency that operates in a decentralized manner.Established in 2009, the organization functions on a technological foundation known as blockchain.The cryptocurrency in question is generated via the process of mining and is renowned for its known level of volatility.| |Ethereum Classic Crypto||ETC||ETC is a blockchain-based platform and decentralized cryptocurrency that emerged as a result of a hard fork from ETH in 2016.The immutability principle of the ETH blockchain is derived from its original version.| |Ethereum Crypto||ETH||ETH is a decentralized blockchain platform and digital currency that was introduced in 2015.
In contrast to BTC, this cryptocurrency possesses a broader scope and is acknowledged for its promotion of smart contracts.Furthermore, it supports the running of decentralized applications.(dApps).| |BTC||CLNE||ETH||ETC||QGREEN||ECO| |Mean||0.001978||0.000198||0.003243||0.001522||0.000395||0.000422| |Std.
Dev.||0.047684||0.046196||0.061735||0.073853||0.012496||0.024384| |Skewness||−0.781854||0.624032||0.175571||0.431585||−0.840059||−0.321265| |Kurtosis||12.70407||16.30657||8.478345||9.616025||14.56515||7.361650| |Jarque-Bera||6734.815||12,451.47||2100.700||3103.195||9520.445||1354.909| |Probability||0.||0.||0.
||0.||0.||0.| |Observations||1673||1673||1673||1673||1673||1673| |Null Hypothesis: Unit Root (Individual Unit Root Process)| |Method||Statistic||Prob.
**| |PP—Fisher Chi-square||256.358||0.0000| |PP—Choi Z-stat||−14.5596||0.0000| |Series||Prob.||Bandwidth||Obs.| |BTC||0.0000||12.0||1671| |CLNE||0.0000||18.0||1671| |ETH||0.0001||8.0||1671| |ETC||0.0000||9.0||1671| |QGREEN||0.0000||9.0||1671| |ECO||0.0000||9.0||1671| |Null Hypothesis: Stationarity| |Method||Statistic||Prob.**| |Hadri Z-stat||−1.38475||0.9169| |Heteroscedastic Consistent Z-stat||−1.25376||0.8950| |Series||LM||Variance||Bandwidth||Obs.| |HAC| |BTC||0.0825||1,223,482||12.0||1672| |CLNE||0.0631||0.081835||19.0||1672| |ETH||0.0659||6917.171||8.0||1672| |ETC||0.0266||6.967334||8.0||1672| |QGREEN||0.1039||1003.904||9.0||1672| |ECO||0.2001||10.26572||8.0||1672| |Market||Test||Stat.||Method||Lags||Date||Results| |ECO | QGREEN||Zt||−4.7 ***||Regime||0||29/11/2018||Integrated| |ECO | CLNE||Zt||−4.72||Trend||1||11/06/2018||Segmented| |ECO | BTC||Zt||−3.87||Trend||1||06/02/2019||Segmented| |ECO | ETC||Zt||−4.02||Trend||1||06/02/2019||Segmented| |ECO | ETH||Zt||−4.07||Trend||1||06/12/2017||Segmented| |QGREEN | ECO||Zt||−4.77 ***||Trend||0||09/01/2018||Integrated| |QGREEN | CLNE||Zt||−4.33||Trend||3||28/09/2018||Segmented| |QGREEN | BTC||Zt||−3.86||Regime||3||06/02/2019||Segmented| |QGREEN | ETC||Zt||−3.71||Regime||1||06/02/2019||Segmented| |QGREEN | ETH||Zt||−3.92||Regime||1||05/03/2019||Segmented| |CLNE | ECO||Zt||−3.28||Regime||5||11/05/2018||Segmented| |CLNE | QGREEN||Zt||−4.06||Trend||5||31/05/2018||Segmented| |CLNE | BTC||Zt||−3.49||Trend||5||31/05/2018||Segmented| |CLNE | ETC||Zt||−3.5||Trend||5||11/05/2018||Segmented| |CLNE | ETH||Zt||−3.52||Trend||5||11/05/2018||Segmented| |BTC | ECO||Zt||−3.72||Trend||3||22/11/2017||Segmented| |BTC | QGREEN||Zt||−3.32||Trend||0||09/11/2017||Segmented| |BTC | CLNE||Zt||−2.77||Trend||3||12/10/2018||Segmented| |BTC | ETC||Zt||−4.3||Trend||0||15/05/2019||Segmented| |BTC | ETH||Zt||−3.84||Regime||0||11/01/2018||Segmented| |ETC | ECO||Zt||−4.28||Regime||4||17/05/2018||Segmented| |ETC | QGREEN||Zt||−4.41||Regime||4||02/08/2018||Segmented| |ETC | CLNE||Zt||−3.94||Regime||5||15/03/2018||Segmented| |ETC | BTC||Zt||−4.58||Regime||0||31/08/2018||Segmented| |ETC | ETH||Zt||−6.14 ***||Regime||0||19/12/2017||Integrated| |ETH | ECO||Zt||−3.91||Regime||4||08/08/2018||Segmented| |ETH | QGREEN||Zt||−4.77 ***||Regime||0||08/08/2018||Integrated| |ETH | CLNE||Zt||−3.81||Regime||5||08/03/2018||Segmented| |ETH | BTC||Zt||−3.91||Regime||3||01/08/2018||Segmented| |ETH | ETC||Zt||−5.97 ***||Regime||1||19/12/2017||Integrated| |Market||Test||Stat.||Method||Lags||Date||Results| |ECO | QGREEN||Zt||−3.35||Trend||3||21/04/2021||Segmented| |ECO | CLNE||Zt||−3.61||Trend||3||02/09/2020||Segmented| |ECO | BTC||Zt||−5.13 **||Regime||0||12/02/2021||Integrated| |ECO | ETC||Zt||−4.3||Trend||2||26/10/2020||Segmented| |ECO | ETH||Zt||−4||Trend||2||26/10/2020||Segmented| |QGREEN | ECO||Zt||−3.61||Regime||2||20/04/2021||Segmented| |QGREEN | CLNE||Zt||−3.43||Regime||3||23/12/2020||Segmented| |QGREEN | BTC||Zt||−3.98||Trend||0||22/07/2020||Segmented| |QGREEN | ETC||Zt||−3.88||Trend||1||26/10/2020||Segmented| |QGREEN | ETH||ADF||−5.6 ***||Regime||1||20/01/2021||Integrated| |CLNE | ECO||ADF||−5.54 ***||Regime||4||13/01/2021||Integrated| |CLNE | QGREEN||Zt||−3.43||Regime||3||05/08/2021||Segmented| |CLNE | BTC||Zt||−5.3 **||Regime||3||15/04/2021||Integrated| |CLNE | ETC||Zt||−5.57 ***||Regime||3||16/04/2021||Integrated| |CLNE | ETH||Zt||−6.38 ***||Regime||3||15/04/2021||Integrated| |BTC | ECO||Zt||−4.11||Regime||0||11/02/2021||Segmented| |BTC | QGREEN||Zt||−3.92||Regime||5||08/01/2021||Segmented| |BTC | CLNE||Zt||−3.29||Regime||0||25/07/2022||Segmented| |BTC | ETC||Zt||−4.87 *||Regime||0||19/04/2021||Integrated| |BTC | ETH||Zt||−7.64 ***||Regime||5||28/04/2021||Integrated| |ETC | ECO||ADF||−7.05 ***||Regime||5||22/04/2021||Integrated| |ETC | QGREEN||Zt||−5.39 **||Regime||5||20/04/2021||Integrated| |ETC | CLNE||Zt||−8.02 ***||Regime||4||26/04/2021||Integrated| |ETC | BTC||Zt||−5.5 ***||Regime||5||19/04/2021||Integrated| |ETC | ETH||Zt||−5.52 ***||Regime||5||08/11/2021||Integrated| |ETH | ECO||Zt||−4.26||Trend||2||13/05/2022||Segmented| |ETH | QGREEN||Zt||−3.93||Trend||4||29/07/2020||Segmented| |ETH | CLNE||Zt||−4.03||Trend||2||13/05/2022||Segmented| |ETH | BTC||ADF||−5.05 **||Trend||5||19/05/2021||Integrated| |ETH | ETC||Zt||−4.93 *||Trend||3||27/05/2022||Integrated| |ECO||QGREEN||CLNE||BTC||ETC||ETH| |ECO||-| |QGREEN||0.7388 ***||-| |CLNE||0.3684 ***||0.2884 ***||-| |BTC||0.0196||0.0099||0.0263||-| |ETC||0.1063 ***||0.0750 **||0.0033||0.5095 ***||-| |ETH||0.0684 *||0.0493||0.0029||0.5723 ***||0.6930 ***||-| |ECO||QGREEN||CLNE||BTC||ETC||ETH| |ECO||-| |QGREEN||0.8128 ***||-| |CLNE||0.6128 ***||0.4974 ***||-| |BTC||0.3832 ***||0.4517 ***||0.3205 ***||-| |ETC||0.2842 ***||0.3359 ***||0.1974 ***||0.7753 ***||-| |ETH||0.3740 ***||0.4254 ***||0.2761 ***||0.7753 ***||0.6795 ***||-| |Tranquil Subperiod||Stress Subperiod||Evolution| |ECO||1/5 integrations||1/5 integrations||=| |QGREEN||1/5 integrations||1/5 integrations||=| |CLNE||0/5 integrations||4/5 integrations||↑| |BTC||0/5 integrations||2/5 integrations||↑| |ETC||1/5 integration||5/5 integrations||↑| |ETH||2/5 integrations||2/5 integrations||=| |Indexes and Cryptocurrencies||t Stat||P(T ≤ t) One-Tail||Results| |ECO | QGREEN||1.331||0.106| |ECO | CLNE||2.230||0.025||Volatility spillover| |ECO | BTC||1.090||0.152| |ECO | ETC||1.085||0.152| |ECO | ETH||1.050||0.159| |QGREEN | ECO||1.279||0.115| |QGREEN | CLNE||2.403||0.019||Volatility spillover| |QGREEN | BTC||1.493||0.083||Volatility spillover| |QGREEN | ETC||1.174||0.134| |QGREEN| ETH||1.133||0.142| |CLNE | ECO||0.681||0.256| |CLNE | QGREEN||0.842||0.210| |CLNE | BTC||0.897||0.195| |CLNE | ETC||0.576||0.289| |CLNE | ETH||0.553||0.296| |BTC | ECO||1.190||0.132| |BTC | QGREEN||1.326||0.109| |BTC | CLNE||2.223||0.027||Volatility spillover| |BTC | ETC||1.094||0.151| |BTC | ETH||1.056||0.159| |ETC | ECO||0.830||0.213| |ETC | QGREEN||0.982||0.175| |ETC | CLNE||1.884||0.044||Volatility spillover| |ETC | BTC||1.037||0.162| |ETC | ETH||1.703||0.249| |Ethereum | ECO||1.248||0.120| |Ethereum | QGREEN||1.393||0.097||Volatility spillover| |Ethereum | CLNE||2.301||0.022||Volatility spillover| |Ethereum | BTC||1.453||0.088||Volatility spillover| |Ethereum | ETC||1.146||0.139| Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s).MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
© 2023 by the authors.
Licensee MDPI, Basel, Switzerland.This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).Share and Cite Dias, R.; Alexandre, P.; Teixeira, N.; Chambino, M.Clean Energy Stocks: Resilient Safe Havens in the Volatility of Dirty Cryptocurrencies.
Energies 2023, 16, 5232.https://doi.org/10.3390/en16135232 Dias R, Alexandre P, Teixeira N, Chambino M.Clean Energy Stocks: Resilient Safe Havens in the Volatility of Dirty Cryptocurrencies.Energies.
2023; 16(13):5232.https://doi.org/10.3390/en16135232Chicago/Turabian Style Dias, Rui, Paulo Alexandre, Nuno Teixeira, and Mariana Chambino.
2023.”Clean Energy Stocks: Resilient Safe Havens in the Volatility of Dirty Cryptocurrencies” Energies 16, no.13: 5232.https://doi.org/10.3390/en16135232 here ..
