Clean Energy Stock Research Articles

Relationships among the Fossil Fuel and Financial Markets during the COVID-19 Pandemic: Evidence from Bayesian DCC-MGARCH Models

This study examined how the relationships among the fossil fuel, clean energy stock, gold, and Bitcoin markets have changed since the COVID-19 pandemic took place for hedging the price change risks in the fossil fuel markets. We applied the Bayesian Dynamic Conditional Correlation-Multivariate GARCH (DCC-MGARCH) models using US daily data from 2 January 2019 to 26 February 2021. Our results suggest that the fossil fuel (WTI crude oil and natural gas) and financial markets (clean energy stock, gold, and Bitcoin) generally had negative relationships in 2019 before the pandemic prevailed, but they became positive for a while in mid-2020, alternating between positive (0.8) and negative values (−0.8). As it is known that negative relationships are required among assets to hedge the risk of price changes, this implies that stakeholders need to be cautious in hedging the risk across the fossil fuel and financial markets when a crisis like COVID-19 occurs. However, our study also revealed that such negative relationships only lasted for three to six months, suggesting that the effects of the pandemic were short term and that stakeholders in the fossil fuel markets could cross hedge with the financial markets in the long term.

A systematic bibliometric review of clean energy transition: Implications for low-carbon development.

More voices are calling for a quicker transition towards clean energy. The exploration and exploitation of clean energy such as wind energy and solar energy are effective means to optimise energy structure and improve energy efficiency. To provide in-depth understanding of clean energy transition, this paper utilises a combination of multiple bibliometric mapping techniques, including HistCite, CiteSpace and R Bibliometrix, to conduct a systematic review on 2,191 clean energy related articles obtained from Web of Science (WoS). We identify five current main research streams in the clean energy field, including Energy Transition, Clean Energy and Carbon Emission Policy, Impact of Oil Price on Alternative Energy Stocks, Clean Energy and Economics, and Venture Capital Investments in Clean Energy. Clearly, the effectiveness of policy-driven and market-driven energy transition is an important ongoing debate. Emerging research topics are also discussed and classified into six areas: Clean Energy Conversion Technology and Biomass Energy Utilisation, Optimisation of Energy Generation Technology, Policy-Making in Clean Energy Transition, Impact of Clean Energy Use and Economic Development on Carbon Emissions, Household Use of Clean Energy, and Clean Energy Stock Markets. Accordingly, more and more research attention has been paid to how to improve energy efficiency through advanced clean energy technology, and how to make targeted policies for clean energy transition and energy market development. This article moves beyond the traditional literature review methods and delineates a systematic research agenda for clean energy research, providing research directions for achieving low-carbon development through the clean energy transition.

The quantile dependence of the stock returns of “clean” and “dirty” firms on oil demand and supply shocks

The paper examines the degree to which firms' stock returns in the energy and technology sectors depend on oil demand and supply shocks by accounting for quantile dependence in shock transmission and causal linkages. Using monthly time-series data from January 2004 to December 2017, our evidence shows that the substitution between oil and clean commodities occurs only in the long-run when the oil market is subject to demand-driven shocks. Unlike oil demand shocks, we demonstrate that oil supply shocks display a relatively lower predictive power for the clean energy stock returns. We also report that firms in the dirty energy sectors display lower returns mainly due to their exposure to exogenous oil shocks, while firms in clean energy and technology sectors are more resilient to demand shocks as their exhibit positive returns in the long-run. Our findings provide evidence and guidance about investments opportunities in clean assets.

Are energy metals hedges or safe havens for clean energy stock returns?

This paper examines the relationship between clean energy stock indices and energy metals that are sensitive to the growth in demand for clean energy solutions, and makes inferences about whether energy metals can act as hedges or safe havens for clean energy stock indices. The sample period is April 2011 to April 2021, a period which saw substantial investments into the clean energy industry as the capital deployed to clean energy generation during this period was about three times higher than the preceding decade. The main results indicate statistically significant non-linear relationships among the markets under study. All energy metals, except cobalt, have a significant positive linkage with clean energy stock indices and such associations do hold during episodes of high volatility. While none of the energy metals under study acts as a hedge for clean energy stock markets, the results support previous evidence on the hedging properties of precious metals, showing that gold and silver serve as hedges for certain clean energy stock indices. These results have important implications amid tremendous growth in clean energy stock investments and the repeating occurrence of periods of uncertainty.

Time-frequency connectedness among clean-energy stocks and fossil fuel markets: Comparison between financial, oil and pandemic crisis

Motivated by lack of empirical research on volatility linkages among clean-energy stock markets and fossil fuel markets during the recent Covid-19 pandemic, the study examines the volatility connectedness network among clean-energy stocks and fossil fuels such as crude WTI, natural gas, gas oil, and fuel oil. In addition, we also compare the influence of financial crises such as the Global Financial Crisis (GFC), oil crisis, and Covid-19 pandemic crisis is driving the volatility connectedness network of energy markets. We apply Diebold and Yilmaz (2012) [1] time-domain and Barunik and Krehlik (Baruník and Křehlík, 2018) [2] frequency-domain approach. The empirical results uncover weak volatility connections among clean-energy stocks and fossil fuel markets. Meanwhile, we find strong volatility interconnectedness between petroleum markets. Further, the results show that most of the volatility spillovers among energy markets persist in the short-run, whereas the findings display weak volatility transmission among the sample markets in the long run. Furthermore, the findings also unveil that contagion effects between the energy markets increase in the crisis periods, intensifying the volatility interlinkages among the sample energy markets. The findings have important significance for energy policymakers and investors.

Accuracy and Predictive Power of Sell-Side Target Prices for Global Clean Energy Companies

Target prices are often provided as a support for stock recommendations by sell-side analysts which represent an explicit estimate of the expected future value of a company’s stock. This research focuses on mean target prices for stocks contained in the Standard and Poor’s Global Clean Energy Index during the time period from 2009 to 2020. The accuracy of mean target prices for these global clean energy stocks at any point during a 12-month period (Year-Highest) is 68.1% and only 46.6% after exactly 12 months (Year-End). A random forest and an SVM classification model were trained for both a Year-End and a Year-Highest target and compared to a random model. The random forest demonstrates the best results with an average accuracy of 73.24% for the Year-End target and 81.15% for the Year-Highest target. The analysis of the variables shows that for all models the mean target price is the most relevant variable, whereas the number of target prices appears to be highly relevant as well. Moreover, the results indicate that following the rare positive predictions of the random forest for the highest target return groups (“30% to 70%” and “Above 70%”) may potentially represent attractive investment opportunities.

Which clean energy sectors are attractive? A portfolio diversification perspective

Clean energy assets have opened up new investment opportunities for market participants. How to construct an efficient clean energy stock portfolio and how it would perform in comparison to traditional assets remain unclear. To fill this gap, this paper investigates optimal allocation within the clean energy stock market. Specifically, the risk and return characteristics of individual clean energy stock sub-sectors are examined. Optimization techniques are used to generate the desired clean energy stock allocation. The performance is compared to the equity market benchmark and dirty energy stocks. The results show that the risk and return on clean energy stock sub-sectors vary significantly. Clean energy stocks generally underperform the overall equity market but outperform dirty stocks. For investors looking to decarbonize their portfolios, the minimum-tail risk strategy is superior to the minimum-variance strategy. The index that tracks developers and operators of renewable energy projects provides the best risk-adjusted returns for investors with moderate risk tolerance and is effective at mitigating the volatility risk of dirty assets. The wind and energy storage indices, however, are the preferred options for reducing the tail risks of dirty assets. The findings shed light on the efficacy of clean energy portfolio optimization strategies and the heterogeneous diversification benefits across clean energy stock sub-sectors, which has important implications for investors constructing clean energy portfolios to meet investment objectives.

Reserve currency and the volatility of clean energy stocks: The role of uncertainty

Clean energy firms are among the most risky firms to invest in. Hence, it is essential to have a clear understanding of the risk profile of clean energy stocks. The main purpose of this study is to examine how changes in the reserve currency (US dollar) value affect the volatility of clean energy stocks in the absence/presence of uncertainty information. To this end, the volatility of clean energy stocks is first obtained by considering uncertainty information in the exponential generalized autoregressive conditional heteroskedastic (EGARCH) volatility estimates. Then, using the autoregressive distributed lag (ARDL) model, this study investigates the effect of reserve currency (US dollar) value fluctuations on the volatility of clean energy stocks. Controlling for monetary conditions, financial stress, and business cycle fluctuations, the study finds that, in the absence of uncertainty information, the volatility of clean energy stocks is not influenced by US dollar fluctuations. However, an appreciation of the US dollar against major foreign currencies significantly increases the volatility of clean energy stocks when uncertainties about economic policy, exchange rate market, and gold market are included in the picture. We find that economic uncertainty mediates the link between reserve currency and the volatility of clean energy stocks. The results shed more light on the risk profile of clean energy stocks and suggest the need for predictable and stable policies to increase the attractiveness of clean energy stocks.

Measuring risk spillovers between oil and clean energy stocks: Evidence from a systematic framework

Increasing concern about the challenges of climate change has triggered rapid growth of private investment into clean energy sectors. Currently, the uncertain global economic environment makes the risk spillovers in the “Oil-Clean energy stocks” system a vital topic to explore. Using a systematic framework involving both Spillover index and Multivariate quantile models, this paper aims at revealing the heterogeneous risk transmission mechanisms between WTI oil futures and 12 clean energy stock indexes over the period of October 2010 to August 2020 from a systematic perspective by considering both regular and extreme conditions. We find asymmetric and time-varying features in the connections between oil and clean energy. The main findings include: (1) responses of extreme downside risks are substantially intensified compared to normal and upside risks; (2) the (downside) VaR response of oil to shocks of clean energy stocks is greater when the oil price is low; (3) the (downside) VaR response of clean energy to oil shocks is heterogeneous especially when the oil price is low. We also find that oil market is a net volatility receiver from clean energy sector overall and especially from the Advanced material and Energy efficiency groups. However, oil is a risk trigger for the Renewable energy group. These findings have significant and abundant implications for investors, portfolio managers, and policymakers to inform their decision-making and promote clean energy investment.

On the pursuit of energy security: evidence from the nexus between clean energy stock price and energy security elements

ABSTRACT Concerns for fossil fuel price volatility, environmental pollution and energy inefficiency drive the formulation of energy policies aimed at attaining energy security. We use a theoretical framework which integrates key elements of energy security into the context of natural capital theory to investigate the causal relationship between Nasdaq clean energy stock price and a range of variables including oil price, natural gas prices, carbon price and energy efficiency. Our autoregressive distributed lag (ARDL) results reveal that clean energy stock price is jointly and individually explained by the variables representing some elements of energy security. Carbon price and energy efficiency emerged as the most important elements of energy security driving the on-going transition from conventional to clean energy sources. Consequently, governments should take environmental sustainability and energy efficiency very seriously when formulating energy policies in the pursuit of energy security and the way they stimulate substitutions between clean energy sources and hydrocarbons.

Energy markets – Who are the influencers?

The energy markets have recently undergone important transformations (e.g. deregulation, technological progress, renewable energy deployment and changing energy consumer behaviour) and witnessed a variety of crisis periods, affecting the relationships among energy commodities and their interactions with clean energy indices. This has implications for price discovery, asset allocation and risk management, which requires in-depth analysis to uncover and identify which energy indices (or forms of energy) lead others or are the most influential, while accounting for asymmetry and non-linearity characteristics. To uncover the complex structure of the relationship across the returns of seven different energy commodities and two clean energy stock indices, we apply Granger causality and transfer entropy in both static and dynamic approaches. The results from the Granger causality analysis identify the influence of the other energy products on natural gas, whereas the transfer entropy analysis reveals the importance of WTI oil and the influence of clean energy indices. Diesel is the most influenced energy commodity. A rolling windows analysis confirms those findings and shows evidence of a time-variation that reflects the impacts of crisis periods, especially the pandemic, on the dynamics of relationships.

How precious metal and energy resources interact with clean energy stocks? Fresh insight from the novel ARDL technique.

To boost the stability of economic and financial aspects along with the apprehensions for sustainability, it is important to promote the development of clean energy stocks around the globe. In the current research, the researchers have examined the impact of oil prices, coal prices, natural gas prices, and gold prices on clean energy stock using the autoregressive distribution lag (ARDL) approach from the year 2011 to the year 2020. The result of daily data analysis specifies that in the long as well as in the short run, gold prices, oil prices, and coal prices have a positive and significant effect on clean energy stock. On the other side, natural gas prices in the long as well as in the short run have a negative and significant effect on clean energy stock. So, the empirical analysis of our study is of interest to investors at an institutional level who aim at detecting the risk associated with the clean energy market through proper financial modeling. Besides, this study opens up a new domain to sustain financial as well as economic prospects by protecting the environment through clean energy stock as the investment in clean energy stocks results in producing a substantial effect on the economy and the environment as well.

Distributional predictability between oil prices and renewable energy stocks: Is there a role for the COVID-19 pandemic?

This research explores the causal relationships between the returns and volatility of oil prices and five clean energy stock indices based on the nonparametric causality-in-quantiles test between October 13, 2010 and September 08, 2020. The analysis is also conducted before and during the COVID-19 pandemic. The findings indicate that over the full and before the pandemic periods, oil returns cause the renewable stock index returns during normal market conditions, but this is not the case in the extreme market conditions. Moreover, all the five renewable energy sectoral stock returns have no predictive power of oil returns under any market conditions. On the other hand, the volatility analysis suggests a significant bidirectional causality between the oil price volatility and the renewable energy stock volatility only at the lower quantiles during the same periods. During the COVID-19 pandemic period, the findings suggest the absence of significant causal relationships between the oil price (returns and volatility) and the renewable energy stocks. However, the causal relationship during the pre-COVID-19 period is close to that reported for the full period. Policy recommendations are therefore proposed based on these results.

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

We examine the multifractal scaling behavior and market efficiency of China’s clean energy stock indexes using an asymmetric multifractal detrended fluctuation analysis (A-MFDFA) and then investigate the tail correlation between this index and the crude oil market via an asymmetric multifractal detrended cross-correlation analysis (A-MFDCCA). First, we reveal that the overall, upward and downward trends of the clean energy stock indexes all have significant multifractal characteristics. The clean energy stock market is far from efficient regardless of whether the fluctuations are small or large. In addition, both upward and downward fluctuations exhibit considerable asymmetry. The significant gap between the downward and overall trends indicates that the downward trend following small-scale fluctuations implies weaker efficiency for investors. Furthermore,based on the sliding market deficiency measure (MDM),we find that the change in efficiency in the three trends significantly depends on the length of the window. In the short term, there is no significant efficiency difference among these three trends; however, in the long term, the asymmetry in the upward and downward trends has gradually increased,especially after December 2018. The results demonstrate that bear markets can offer considerably more opportunities for obtaining excess profits. Finally, we reveal that the cross-correlation between the trends of crude oil prices and low-carbon indexes exhibits significant multifractal characteristics. When the crude oil market is in a bull market or the low-carbon energy market is in a bear market, especially in a larger-scale fluctuation, investors should pay attention to the long-term influence of the counterparty market and carry out a hedging operation to avoid risks.

Do Green Bonds Act as a Hedge or a Safe Haven against Economic Policy Uncertainty? Evidence from the USA and China

Economic policy uncertainty and particularly COVID-19 has stimulated the need to investigate alternative avenues for policy risk management. In this context, this study examines the dynamic association among economic policy uncertainty, green bonds, clean energy stocks, and global rare earth elements. A dynamic conditional correlation-multivariate generalized autoregressive conditional heteroscedasticity (DCC-MGARCH) model was used to gauge the time-varying co-movements among these indices. The analysis finds that green bonds act more as a hedge than a safe haven against economic policy uncertainty (EPU). In the case of diversification, green bonds work as diversifiers with clean energy stocks and rare earth elements during COVID-19 and in the whole sample period. Additionally, clean energy stocks and rare earth elements show safe haven properties against EPUs. This study contributes to the hedging and safe haven literature with some new insight considering the role of green bonds and clean energy stocks. Additionally, the outcomes of the research contribute toward the literature of portfolio diversification theory. These findings pave the way for not only US investors to hedge long-term economic policy risk by investing in green bonds, but also for China and the UK, as these financial assets (green bonds, clean energy stocks, and rare earth metals) and EPU are long-term financial and economic variables.

Co-Movements between Eu Ets and the Energy Markets: A Var-Dcc-Garch Approach

This paper analyzes the co-movements of prices of fossil fuels, energy stock markets and EU allowances. This analysis is conducted in order to identify the spillover effect of volatility and correlation among these financial markets, and to provide a scientific basis that shows the interest of incorporating sustainable assets in the design of minimum risk strategies of investment. To achieve this goal, we have used a Vector Autoregressive-Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroscedasticity (VAR-DCC-GARCH) model that also incorporates a stock index of industrial companies as a leading indicator of the level of economic activity. In addition, the paper conducts an impulse response analysis to determine how unexpected shocks to prices are propagated along time, and, in particular, how they affect prices of the others, both in mean, variance and correlation. Therefore, the results of this one- and two-dimensional analysis allow for the study of short and long run dynamics of the relationship among those prices, thus, providing greater meaning and information for investors, which has implications for building their portfolios. The analyzed period was from January 2010 to February 2021, so that the data include half of phase II, full phase III and the onset of phase IV of the EU ETS, as well as the COVID-19 outbreak in the European context. We also analyzed whether the EUA price impulses the demand of clean energy stocks, which has important implications for the objective of triggering the investment in clean energy. Our results show the transmission mechanism of all of those prices, which are relevant not only for investors but also for policymakers to construct an early-warning system, revealing the most important transmission channels. Moreover, from an investment viewpoint, we observe a decline in dirty energies and a rise in the clean energy market, which might be an indication of the progress towards the energy transition to renewables sources within a circular economy perspective. Therefore, this shows that the EU ETS is achieving its goals, and that clean energy companies, aligned with their role towards socially responsible initiatives, are also gaining acceptance in terms of investments, which would be beneficial for the environment.

Do oil price changes really matter for clean energy returns?

This study analyses the dynamic impacts of oil price changes on clean energy companies' stock returns by applying a connectedness network approach based on evidence from the European perspective. The results suggest that although changes in crude oil prices and clean energy stocks returns move towards the same direction, oil returns mainly act as a net information receiver in the crude oil-clean energy nexus system. The dynamic results suggest that information interdependence for crude oil returns and clean energy companies' returns remains at a relatively high level regardless of time variation. Moreover, there is obvious asymmetry in information connectedness for oil and clean energy companies’ returns, which shows the apparent leverage effect and suggests that the impact of bad news on information connectedness is greater than that of good news. These findings can provide micro insights for new energy policy makers and market participants in clean energy industries.

The dynamic dependence of natural gas and renewable energy stock markets

We measure the dynamics dependence structure between the European renewable energy stock (ERIX) market, natural gas, and other clean energy markets from 2008 to 2021 by adopting static and time-varying copula approaches. Empirical results show strong and positive dependence between ERIX and S&P global clean energy stock markets symmetric tail dependence, which indicates S&P clean energy assets provide limited hedging condition on the ERIX market and extreme upward and downward clean prices have a similar impact on the ERIX market. Furthermore, our evidence on the co-movement mechanisms between European renewable energy and clean energy markets is helpful for policymakers and investors. Governments need to adopt fiscal policy tools for clean energy firms when renewable energy prices rise (fall) together. Investors can assess and control potential risk contagion between European renewable energy stock markets and other clean energy markets via information about co-movement mechanisms.

Does Investor Sentiment Affect Clean Energy Stock? Evidence from TVP-VAR-Based Connectedness Approach

We investigated the connectedness of the returns and volatility of clean energy stock, technology stock, crude oil, natural gas, and investor sentiment based on the time-varying parameter vector autoregressive (TVP-VAR) connectedness approach. The empirical results indicate that the average total connectedness is higher in the volatility system than in the return system. The investor sentiment has a weak impact on clean energy stock. Our results show that the dynamic total connectedness across assets in the system varies with time. Furthermore, the dynamic total connectedness increases significantly during financial turmoil. Dynamic total volatility connectedness is more sensitive to financial turmoil. By comparing the connectedness estimated by the TVP-VAR model with the rolling-window VAR model, we find the dynamic total return connectedness of the TVP-VAR model is similar to the estimated results of a 200 day rolling-window VAR model.

Are oil-clean energy and high technology stock prices in the same straits? Bubbles speculation and time-varying perspectives

Motivated by the drive to improve the performance and growth of clean energy technology amidst related high-tech innovations, the vulnerability of clean energy and high-tech stock prices to oil shocks is examined, by illustrating the potential bubbles and time-varying interactions among the commodities over the period from January 2004 to December 2017. In this regard, we contribute to the literature in two aspects. First, we analyze an empirically important issue with the SADF (Supremum Augmented Dickey-Fuller) approach for explosive bubbles in oil price, clean energy, and high-tech stock prices. Second, the Markov Chain Monte Carlo (MCMC) approach of the Bayesian time-varying parameter Vector Autoregressions model with stochastic volatility (TVP-SVAR) technique is used to account for time-varying and state dependent interactions between commodities. We found that the time varying behavior of the dependence among clean energy, high technology stocks and oil prices is mainly due to major bubbles identified in the underlying series. We established contrasting evidence between the responses of clean energy and high-tech stocks to oil disruption shocks. Moreover, the stock return volatilities of high technology stocks have no effect on investors’ expectations of clean energy returns across different time horizons. Overall, this study presents significantly relevant policy guideline.