Clean Energy Stock Research Articles

Green bonds and clean energy stocks: Safe havens against global uncertainties? A wavelet quantile-based examination

The renewable energy sector has seen rapid investment growth over the last decade, with Green Bonds (GB) and Clean Energy Stocks (CLE) emerging as critical components of sustainable finance. Using daily data from 2013 to 2023, this study investigates the diverse roles of GB and CLE as potential safe havens under Geopolitical Risks (GEO) and Economic Policy Uncertainty (ECU). Applying an advanced Wavelet Quantile Regression (WQR) approach, we assess their effectiveness across different time horizons and risk conditions. The findings reveal that GB consistently offer stronger protection against economic risks, particularly in the short to mid-term, while CLE are more effective in shielding against geopolitical risks. Over longer periods, GB maintain their safe haven status, while CLE’s protective capabilities diminish. The results also highlight that GB and CLE behave differently across quantiles, with GB showing stability across extreme market conditions, making it a more reliable hedge overall. This study offers valuable policy insights for key stakeholders. Policymakers are encouraged to integrate GB and CLE into sustainable finance strategies, investors can mitigate risk by blending these assets, and portfolio managers should adjust allocations dynamically based on evolving market conditions. The results underscore the importance of green assets as hedges in times of global uncertainty, contributing to the growing literature on sustainable finance.

Testing the Diversifying Asset Hypothesis between Clean Energy Stock Indices and Oil Price

Testing the Diversifying Asset Hypothesis between Clean Energy Stock Indices and Oil Price

Attractiveness of clean energy stocks in Europe

The Ukrainian conflict has put in evidence the need to understand if oil and gas shocks increase the attractiveness of clean energy investments in Europe. If that is the case, clean energy investments are both the solution to the end of the month problem in Europe and to the end of the world problem due to climate change. Using monthly data for the period 2008–2021, we pinpoint supply and demand shocks in the oil and gas markets affecting Europe and investigate their effects on clean energy stock returns using a structural VAR model. We complete the analysis by studying the connection of these shocks with geopolitical risk. Our findings show that, clean energy stocks are positively impacted by a negative shock in the gas supply to Europe but are not significantly affected by a negative shock in global oil supply. Moreover, clean energy stocks are positively influenced both by oil-specific and gas-specific demand shocks. Finally, the favorable impact of economic demand shocks on clean energy returns lasts more in the oil price model than in the gas price model. Regarding geopolitical risk, we find a strong connectedness, particularly with shocks in the European gas market. These findings imply that, in Europe, clean energy is indeed a substitute to oil and gas in times of crisis. We are then likely to observe an increase in clean energy returns as prices of fossil sources spike.

Quantile and time-frequency spillover between rare earth and its interlinked markets: A geopolitical perspective

Underlying vulnerability of geopolitical risk for rare earth markets and its interlinked markets motivated this research is to explore their nexus by using different connectedness models. In doing so, the current study employed three diversified set of methodologies namely, quantile vector autoregression, BK framework, and cross-quantilogram to investigate connectedness structure across different market conditions. The dataset of this study spanning from March 29, 2018 to April 28, 2023, covering several turmoil periods. Surprisingly, the empirical findings reveal significant evidence of connectedness between all markets. Notably, in lower quantile, geopolitical uncertainty, rare earth, and green energy stocks are the major transmitters, while oil is the major receiver. At the same time, rare earth, clean energy, technology, natural resource markets, and all green energy tokens except carbon are the significant transmitters in the upper quantile. In contrast, BK measures demonstrate that total frequency connectedness is more significant in the short and long run. While, cross-quantilogram results reveal that connectedness is more pronounced in the long run for rare earth, clean energy, technology, and mining stocks. Specifically, it is important to note that spillover effect are more substantial and differ across different crisis episodes. In addition, large swings in the connectedness index between geopolitical risk and chosen markets is observed after the escalation of the Russia-Ukraine War. The empirical results offer several important implications for investors and policymakers to apply effective risk management techniques and safeguard investments especially during economic turbulence.

Quartile risk dependence between clean energy markets and the U.S. travel and leisure index

ABSTRACT We employed the Cross-quantilogram method for the first time to assess the cross-quantile risk relationship among the clean energy market and the Dow Jones U.S. Travel & Leisure Index during the period from 2014 to 2023. This investigation aimed to explore the asymmetric nature of the risk-dependence structure. Our findings reveal that, under market stability conditions, the U.S. Travel & Leisure index exhibited the highest correlation with all clean energy stocks. However, the risk correlation with the NASDAQ OMX Geothermal, NASDAQ OMX Solar, and NASDAQ OMX Wind Indices significantly decreased during longer investment horizons and extreme quantiles. Notably, in the extreme tail, the correlation between specific clean energy markets and the U.S. Travel & Leisure index displayed heterogeneity. Our results have new practical implications for policymakers and investors who need to capture the risk connection among clean energy indices and the U.S. Travel and Leisure sector.

Do clean energy stocks diversify the risk of FinTech stocks? Connectedness and portfolio implications

The FinTech sector is growing rapidly, prompting a need to explore effective investment diversification strategies for stocks in this sector. The existing literature has identified the benefits of using clean energy stocks to diversify stock portfolios and the purpose of this research is to estimate how useful clean energy stocks are for diversifying an investment in FinTech stocks. This study uses a QVAR model to estimate the dynamic return connectedness between FinTech stocks and clean energy stocks for the period September 2016 to April 2024. Total connectedness is time varying and is higher in the tails than at the median. The onset of the COVID-19 pandemic had a large but short-term impact on connectedness. Under normal market conditions, systemic risk increases by 3.5% per year. FinTech is a net transmitter of shocks to nuclear energy but is mostly unaffected by shocks from wind, solar, and nuclear energy stocks illustrating the diversification benefits of these sub-sectors. Portfolio analysis shows that adding solar, wind, and nuclear energy to a portfolio with FinTech can produce higher risk adjusted returns and lower drawdown than an investment solely in FinTech stocks. These results are robust across various portfolio rebalancing frequencies (daily, weekly, monthly). For example, a minimum connectedness portfolio rebalanced daily has an average annual return of 11% and a Sharpe ratio of 0.37. These values are higher than their respective values for an investment solely in FinTech stocks (5.4%, 0.11). Thus, clean energy stocks do provide diversification benefits for investments in FinTech stocks.

Is gold a preferable diversifier of cleaner equity risk across diverse scenarios? Evidence from multidimensional connectedness and spillover measures

The study aims to explore the risk management potential of historical (gold and crude oil) and modern (bitcoin) assets against the shocks in clean energy stocks. Specifically, the quest related to which asset(s)? In what proportion? In which market condition? and for what time preference? should be held with cleaner equity. The exploration is based on the presumptions that conventional and cleaner stocks are alike and that financial markets (assets) have an asymmetric interdependence. We also assume that the interdependence among the considered markets is driven by various events over time and distinct investment horizons. To that end, we employ a generalized vector autoregressive-based overall, time-varying, asymmetric, and frequency-based connectedness measures and DCC-GARCH model to the daily data ranging from October 15, 2014 to April 12, 2023. The spillovers across the considered markets confirm the similarity of conventional and cleaner stocks and the asymmetry hypothesis, suggesting that downside shock transmission (bad spillovers) outperforms upside shock transmission (good spillovers). The time-varying results suggest that connectedness among the considered markets is higher, and asymmetric during Brexit-2016, the COVID-19 pandemic, Russia's invasion of Ukraine in early 2022, and the monetary policy tightening of 2022 and 2023. Finally, we show that gold is a preferable diversifier of cleaner equity risk on average, during distress events, downside markets, and across all investment horizons. The study has implications for financial market participants to strategically manage the cleaner equity risk with gold in diverse market scenarios.

Do climate risks affect dirty–clean energy stock price dynamic correlations?

Prior studies have extensively exhibited an interest in exploring the connectedness between dirty and clean energy stock prices alongside the drivers of such price connectedness, shedding light on hedging strategies for finance practitioners. Nevertheless, no empirical research has examined whether climate risks, the emerging indicator for investors to handle the divestment of dirty energy stocks, have affected the time-varying dirty–clean energy equity price nexus. This study fills this gap by innovatively identifying dynamic conditional correlations (DCCs) between dirty and clean energy stock prices. An ARDL/NARDL model is applied to assess whether the climate risks affect such correlations by controlling for business cycles, funding liquidity, USD values, and oil market sentiments. Overall, we detect an undeniable negative impact of climate risks on the positive dirty–clean energy price dynamic correlations. Additionally, the NARDL model results reveal that a rise in federal fund rates exerts higher effects on the dirty–clean energy stock price comovements. Our findings suggest the strengthened potential of hedging clean energy stocks against dirty energy equities in case of escalating climate risks and heightened fossil fuel price volatilities. Furthermore, substantial attention is required to account for monetary policies' asymmetric effects on clean energy investment.

Do dirty and clean energy investments react to infectious disease-induced uncertainty?

Following the outbreak of novel coronavirus, numerous studies have assessed the effect on global financial markets. However, investigations into whether, and to what extent, dirty and clean energy asset classes are sensitive to contagious diseases are rare. This study aims to fill this gap in the literature. We measured the effects of contagious viruses using the recently introduced infectious disease-related uncertainty index (EMVID). Our data include the iShares Global Energy ETF and clean energy stock indices from leading economies such as the United States, the European Union, Japan, and China. Employing the GARCH-MIDAS model, we find that the uncertainty associated with infectious diseases has a significant positive (negative) effect on the realized volatility (RV) of dirty (green) assets. This finding is novel given that it indicates the potential for “green recovery” in the post-pandemic era. Our findings further document that EMVID has significant predictive content for the volatility of these assets and that inserting the EMVID index into the GARCH-MIDAS process produces better volatility predictions than other uncertainty measures, including the crude oil volatility (OVX), geopolitical risk (GPR), and economic policy uncertainty (EPU) indices. Hence, the EMVID provides additional information not contained in the OVX, GPR, or EPU during the pandemic period. Our findings will be useful for energy market participants to make appropriate asset allocation decisions during pandemics.

Attention to climate change and eco-friendly financial-asset prices: A quantile ARDL approach

In recent years, heightened global attention to climate change threats has spurred a rising tide of investors towards eco-conscious investment alternatives, reflecting a persistent urgency to align financial activities with sustainability objectives. This paper examines the potential role of people's attention to climate change concerns in explaining the price dynamics of various environmentally friendly financial assets, including solar energy stocks, clean energy stocks, sustainable stocks, low-carbon footprint stocks, and green bonds. The sample period is from January 03, 2014 to March 08, 2024. The principal component analysis is used to construct a proxy indicator for climate change-related attention, while the quantile ARDL approach is adopted to assess the relevance of this attention not only across short- and long-term horizons but also amidst normal, bearish, and bullish market conditions. The results reveal that eco-friendly financial assets are sensitive to public focus on climate change in the short run. The observed sensitivity, nonetheless, tends to fade away over the long term. For most assets examined, the magnitude of this sensitivity is not symmetric across quantiles; rather, it appears to vary depending on the prevailing market states. Our findings bear important implications for both investors and policymakers.

Characteristics and influencing factors of risk spillover effects across clean energy stock prices: A comparative analysis during four periods of the COVID-19 pandemic

This paper explores the characteristics and influencing factors of risk spillover effects across clean energy stock prices during four periods of the COVID-19 pandemic, using the TVP-VAR-DY model and the TVP-SVAR-SV model. The empirical results suggest that the risk spillover level across clean energy stock prices declined with the outbreak of the COVID-19 pandemic, and the volatility was initially violent. However, as the pandemic began to mitigate and recovery ensued, the risk spillover level rebounded and became stable. After the COVID-19 pandemic, smart grids and three renewable energy sources (photovoltaics, wind energy, and hydropower) were the main transmitters of net risk spillover effects across clean energy stock prices, while the clean and efficient use of traditional energy was always a net risk spillover effects absorber. Regarding influencing factors, the impact of structural factors (net power generation of various types of clean energy) on the risk spillover effect across clean energy stock prices is generally greater than that of external factors (economic factors and political factors), and these effects last longer. Finally, among structural factors, the net power generation of two renewable energy sources—wind energy and photovoltaics—has a greater impact, especially during the outbreak and mitigation period of the COVID-19 pandemic. Beyond renewable energy, the net power generation from smart grids and nuclear power had a significant impact during the post-COVID-19 recovery period. These new findings suggest useful implications for investors and policymakers in the clean energy markets.

Side Effects and Interactions: Exploring the Relationship between Dirty and Green Cryptocurrencies and Clean Energy Stock Indices

This study aimed to assess whether renewable energy cryptocurrencies such as Cardano (ADA), Ripple (XRP), IOTA (MIOTA), and Stellar (XLM) can be considered hedging assets and safe havens for cryptocurrencies classified as "dirty", such as Bitcoin Cash (BCH), Bitcoin (BTC) Litcoin (LTC), Ethereum (ETH), Ethereum Classic (ETC) and the clean energy stock indices WILDERHILL Clean Energy (ECO) and Clean Energy Fuels (CLNE), from July 6, 2018, to July 6, 2023. The results show that the movements decreased significantly during the Stress period, which includes the events of 2020 and 2022. The Cardano cryptocurrency shows moderate movements, indicating stability and diversification, while Stellar shows moderate movements that suggest resilience. Conversely, XRP shows varied movements, requiring some caution, while IOTA stands out for significant movements associated with sustainable assets. These results interest players operating in these markets when they want to diversify and rebalance their portfolios.

Evidence on Price Formation In Financial Markets: A Multitemporal Analysis

Background: Although clean energy stocks have become a popular new asset class for market players, relatively little research has been done on risk management strategies for investors in clean energy stock markets. Gold and Silver are examples of precious metals typically considered suitable investments to hedge against uncertainty. Therefore, their demand tends to rise when stock markets decline. Purpose: The study aims to analyse the influence of precious metals (Gold, Silver and Platinum) and green stock indices (Clean Energy Fuels, Nasdaq Clean Edge Green Energy, S&P Global Clean Energy, WilderHill Clean Energy) on price formation during different economic periods between 1 January 2018 and 23 November 2023. Methods: The periods analysed were Tranquil, 2020 pandemic, pre-conflict and Conflict (Russian invasion of Ukraine). To this end, a VAR Granger Causality/Block Exogeneity Wald model was estimated to identify different influence patterns during each period. Results: During the Tranquil period, 15 movements affecting prices were observed, with Silver significantly influencing all its peers, while Gold impacted Platinum, S&P Global, Nasdaq and WilderHill. Platinum was the asset that received the most shocks from its peers. During the 2020 pandemic, there was an increase to 26 movements, with the S&P Global and Nasdaq Clean indices influencing most of their peers. Gold and Platinum continued to be the assets most influenced by their peers. In the pre-conflict period, 11 movements were identified, with the highlight being the mutual influence between Gold, the Nasdaq and WilderHill indices, and Platinum. During this period, Silver and S&P Global only affected the price formation of Platinum and Clean Energy Fuels, respectively. During the Conflict period, 18 movements were observed, highlighting the influence of Platinum and S&P Global on the price formation of various assets. Gold was the asset that received the most shocks from its peers during this period. Conclusion: the results indicate changes in the interactions between assets throughout the different economic contexts, highlighting the importance of understanding these dynamics to make more informed decisions.

Pricing behavior of clean energy stocks? Some trading implications

Mispricing and price adjustment to shocks are common features of stocks, which arise due to various types of shocks. Understanding mispricing and price adjustment mechanism can guide trading strategy design. Our hypothesis is that clean energy stocks are characterised by mispricing and that there are unique stocks in the sense that they move first when exposed to shocks. We employ a price discovery model to rank clean energy stock indices in terms of those that adjust to prices faster than those that do not. We then identify mispricing in those stocks using a market risk factor model. Utilizing time-series data on 11 clean energy stock indices, we propose eight trading strategies, and show that they offer annualized returns in the −5.37% to 61.10% range. Six of the eight strategies offer profits close to 15% or more, beating the buy and hold strategy. Trading strategies devised on mispricing suggest that two of the clean energy stocks can potentially beat the buy-and-hold strategy, offering annualized returns in excess of 13%. Because our results guide investors towards making profits from trading clean energy stocks, the implication is that the trading strategies can enhance the green finance market.

Asymmetric impacts of coal prices, fintech, and financial stress on clean energy stocks

The emergence of fintech has revolutionized financial systems, alleviating financial stress through innovative solutions and diversification. This shift towards clean energy stocks has reduced demand for coal, impacting coal prices significantly and highlighting the pressing need to preserve mineral resources for sustainable, cleaner energy alternatives. Therefore, the study examines the interconnectedness between coal price, fintech, financial stress, and green energy stock using quantile-based regression. The findings reveal that the impact of coal price, fintech, and financial stress on the clean energy stock is heterogeneous across all levels of quantiles. Specifically, the findings indicate that variations in the coal price positively influence the clean energy stock index, yet this association displays asymmetry across quantiles. Fluctuations in the fintech index contribute to the susceptibility of renewable energy stock prices. The financial stress index exhibits a consistently adverse influence on clean energy stock across quantiles ranging from lower to higher orders. The study findings would guide stock market investors, ecological activists, and policymakers at administrative levels aiming to promote clean energy investment while controlling for the adverse outcomes from financial stress.

A global perspective on the nexus between energy and stock markets in light of the rise of renewable energy

The rise in renewable energy and the associated divestment initiatives from fossil fuel around the world have become prominent in combating climate change. This paper revisits the relationship between energy and stock markets by accounting for renewable energy through the use of the Divisia index method. The paper contributes to the literature by using three renewable energy tariffs rather than relying on clean energy stocks as a proxy for renewable energy. We retrieve data for both the price and consumption of fossil energy (oil, coal and gas) and renewable energy (solar, wind and hydro). We estimate a Vector Autoregressive (VAR) model over the period 2000–2019 across 25 countries globally. Our study identifies a significant time-dependent dynamics between renewable energy and both the energy stock and carbon markets, while fossil energy has no significant influence on those markets. The increasing influence of renewable energy in this nexus signals to policymakers that investors have started to shift their focus of attention from fossil to renewable energy.

Global impact of geopolitical oil price uncertainty and associated commodity prices on clean energy stocks

Theoretically, geopolitical risk and policy uncertainties can directly affect energy markets. Since fluctuations in it lead to cost the of clean energy sources as they compete with traditional energy. Regarding this, our study aims to scrutinize the impact of geopolitical oil price uncertainty on clean energy stocks by controlling the influences of economic policy uncertainty, gold, natural gas, and coal prices. For that purpose, the study utilized monthly data from July 2007 to September 2020 and employed a machine learning method, namely kernel-based regularized least squares approach. Empirical analysis reveals that geopolitical oil price uncertainty and coal prices have a nonlinear positive effect on clean energy stock prices. It is also found that the impact of global economic policy uncertainty, gold, and natural gas prices on clean energy stock prices is nonlinear and negative. The implication signifies that clean energy stock prices are hampered by economic policy uncertainty, and gold, and natural gas prices, thus hindering the development of clean energy sources. Similarly, for the robustness of the study, the quantile regression approach and findings reveal similar outcomes to that of the KRLS model. Based on these findings, policy implications that potentially aid renewable energy investments are put forward. The study also guides investors, financial advisors, and portfolio managers for better decision-making in consideration of uncertainties and associated fluctuations in energy markets and commodity prices.

The asymmetric impacts of artificial intelligence and oil shocks on clean energy industries by considering COVID-19

Can grand environmental goals still be achieved when the 1.5 °C climate target and Industry 4.0 encounter the COVID-19 pandemic? Exploring the impacts of artificial intelligence (AI) and crude oil shocks as critical drivers on clean energy stocks may provide fresh insights to tackle this question. This paper explores the asymmetric impacts of positive and negative changes in the AI index and different oil shocks on clean energy stock sub-sectors by adopting the non-linear autoregressive distributed lag (NARDL) model. Our investigation of a sample reveals a significant positive long-run relationship between the AI index and clean energy stocks. Additionally, the impacts of AI and oil shocks on clean energy stocks vary broadly across different sub-sectors. The COVID-19 pandemic has strengthened the cointegration relationship and reduced the long- or short-term asymmetry among the AI index and clean energy stocks. This study incorporates the AI index into the research framework of oil and clean energy, which has not been explored in previous research, and is of significant importance for clean energy investment and provides guidance for market participants.

Do climate change risks impact clean energy stock prices? Evidence from machine learning

Do climate change risks impact clean energy stock prices? Evidence from machine learning

Clean energy stock price forecasting and response to macroeconomic variables: A novel framework using Facebook's Prophet, NeuralProphet and explainable AI

The clean energy market analysis has seen a strong surge in the post-Paris agreement owing to its undeniable environmental sustainability. The present work investigates the predictability of clean energy investment in the US market by selecting eight sectoral stock indices. The predictive exercise is carried out separately during pre-COVID-19 and COVID-19 timelines to draw key behavioral aspects of the underlying sectors. After scrutiny of previous research, we identify a set of technical and macroeconomic variables as explanatory constructs. We then utilize Facebook's Prophet and NeuralProphet to find future figures for the clean energy indices. Finally, Explainable Artificial Intelligence (XAI) is used to draw deeper insights into the contribution patterns of the constituent explanatory variables and obtain their relative importance. The findings suggest that the future movements of the sectoral clean energy assets can be predicted with a very high level of accuracy. Also, the predictability marginally improves during the COVID-19 pandemic despite the unprecedented uncertainty. Technical indicators appear to be the dominant features, while market sentiment and fear exert significant influence.