Clean Energy Index Research Articles

Does climate risk as barometers for specific clean energy indices? Insights from quartiles and time-frequency perspective

This study presents the first analysis of the nexus between the Southern Oscillation Index (SOI), a measure of climate risk, and segmented clean energy indices (such as solar, renewable, and bioenergy). Our research findings indicate that (i) the Granger quantile causality significance of SOI on segmented clean energy indices is asymmetric across different conditional quantiles. Significant predictability of SOI is observed only at the 0.25 and 0.75 quantile levels for all segmented clean energy indices, except for the WilderHill Clean Energy Index and NASDAQ OMX Fuel Cell Index. (ii) The clean energy market is significantly influenced by SOI under bullish market conditions. Impacts of SOI on all clean energy markets are nearly negligible when clean energy indices are at the median and lower quantile levels. (iii) The influence of strong La Niña episodes on segmented clean energy indices is more pronounced than during periods of intense El Niño phenomena. (iv) SOI exhibited a positive correlation at mid-term and long-term frequencies with segmented Clean Energy sectors, excluding bioenergy, for the majority of the sample period. Our conclusions provide deeper insights for investors managing clean energy investments in extreme climate conditions. Additionally, they offer useful information for policymakers to formulate viable economic policies addressing climate change, ensuring energy security, and facilitating a safer transition to clean energy.

Asymmetric dependency among US national financial conditions and clean energy markets

This paper examines the relationship between the US National Financial Conditions Index (NFCI) and the clean energy industry using quantile and frequency connectedness, cross-quantile, and wavelet quantile correlation (WQC) techniques. Results reveal (a) a stronger dependence between the NFCI and clean energy under bullish market states. Moreover, the total connectedness between the NFCI and clean energy mostly exhibits time-varying characteristics. In particular, clean energy has a greater spillover effect than the NFCI. (b) Dynamic frequency total connectedness at extreme quantiles provided a more comprehensive view of structural shocks in financial markets, and major crises, such as COVID-19, significantly amplified this connectedness. Overall, the WilderHill Clean Energy Index and the NASDAQ OMX Renewable Energy Index demonstrate substantial potential for hedging financial conditions. (c) The cross-quantile correlation results revealed an asymmetric dependency, demonstrating a sustained significant positive relationship between the NFCI and clean energy index (CEI) across the relative higher quantiles and middle quantiles. The WQC showed that the NFCI and specific CEIs tended to exhibit the strongest positive correlations in nonextreme quantiles and lower frequencies. These results can be of considerable interest to various financial market participants.

Extreme time-frequency connectedness between energy sector markets and financial markets

The renewable energy market has developed into a distinct asset class that has garnered significant attention from investors worldwide. This study investigates the connectedness between the S&P 500 and various energy sectors, including the Green Bond (Green Bond), S&P GSCI Carbon Emission Allowances Index (GSCI EUA), S&P GSCI Biofuel (GSCI Biofuel), and S&P Global Clean Energy Indices (Global Clean Energy). Combining the quantile connectedness approach of Ando et al. (2022) and the spillover index developed by Chatziantoniou et al. (2022), we investigate the quantile time-frequency connectedness among these markets. Our findings reveal that the impact of tail risk spreading to the energy sector during extremely challenging market conditions is significantly amplified. Most tail risk spillovers between the energy sectors involve short-term risk spillovers, as seen by analyzing the frequency domain. The impact of tail risk spillover is stronger during extreme situations, showing a short-term asymmetry. Global Clean Energy and the S&P 500 play important roles in spreading risk to other sectors, known as tail risk spillover. They add to the overall risk that affects the network system. On the other hand, other energy sectors (GSCI EUA and GSCI Biofuel) as well as green bonds act as net recipients by receiving the risk rather than spreading it further, potentially providing portfolio diversification. It is essential to understand how these key players influence risk in the financial system. Utilizing a variety of techniques to assess portfolio performance shows the importance of understanding the return transmission mechanism in the process of developing a portfolio and suggests that Clean Energy, given its relative independence, may make an excellent diversification investment.

Using machine learning to forecast clean energy, commodities, green bonds and ESG index prices: How important is financial stress?

PurposeThis study investigates clean energy, commodities, green bonds and environmental, social and governance (ESG) index prices forecasting and assesses the predictive performance of various factors on these asset prices, used for the development of a robust forecasting support decision model using machine learning (ML) techniques. More specifically, we explore the impact of the financial stress on forecasting price.Design/methodology/approachWe utilize feature selection techniques to evaluate the predictive efficacy of various factors on asset prices. Moreover, we have developed a forecasting model for these asset prices by assessing the accuracy of two ML models: specifically, the deep learning long short-term memory (LSTM) neural networks and the extreme gradient boosting (XGBoost) model. To check the robustness of the study results, the authors referred to bootstrap linear regression as an alternative traditional method for forecasting green asset prices.FindingsThe results highlight the significance of financial stress in enhancing price forecast accuracy, with the financial stress index (FSI) and panic index (PI) emerging as primary determinants. In terms of the forecasting model's accuracy, our analysis reveals that the LSTM outperformed the XGBoost model, establishing itself as the most efficient algorithm among the two tested.Practical implicationsThis research enhances comprehension, which is valuable for both investors and policymakers seeking improved price forecasting through the utilization of a predictive model.Originality/valueTo the authors' best knowledge, this marks the inaugural attempt to construct a multivariate forecasting model. Indeed, the development of a robust forecasting model utilizing ML techniques provides practical value as a decision support tool for shaping investment strategies.

Sustainability indices nexus: Green economy, ESG, environment and clean energy

In this article, we examine the spillover effects and connectedness among four prominent sustainability-related indices. We analyze daily data spanning from August 8, 2014, to April 12, 2023, to explore the interrelationships among the Green Economy Index, the World Environmental, Social, and Governance Index, the Global Environment Index and the Global Clean Energy Index. Particularly, we observe a clustering characteristic where both the Green Economy Index and the World Environmental, Social, and Governance Index exhibit a net-contributing behavior, especially during and after the COVID-19 outbreak. Global Environment Index, on the other hand, exhibits a net-receiving role, which became strong during the COVID-19 outbreak. Further, the Global Clean Energy Index demonstrates a neutral role, although it shows a net-receiving trend after the year 2022. Finally, dynamic analysis confirms the evolving nature of these interconnectedness measures, highlighting their sensitivity to events and shifting economic conditions over time.

Strategic Management of Clean Energy Investments: Financial Performance Insights by Using BWM-based VIKOR and TOPSIS Methods

Market share of renewable energy companies increases with the increase in green-energy production. Companies increase their investments depending on the increase in energy production and can provide capital by offering the company to the public. Companies are traded in the exchange market in order to fund their increasing investments and access a large investor base. The increase in the market values of clean energy companies in recent years draws attention of investors. The study aims to identify key financial indicators that influence company growth and investment value by analyzing companies listed in the S&P North America and Europe Clean Energy Index. Therefore, the study evaluates the financial performance of clean energy companies using Best-Worst Method (BWM)-based VIKOR and TOPSIS methods to provide investors with a comprehensive assessment of investment opportunities in the clean energy sector. Also, the comparison of the two methods will contribute to the literature. The sample of this study consists of 10 big companies in S&P North America and Europe Clean Energy Index. Financial tables and exchange market data of the companies for the years 2019, 2020, and 2021 were used in this study. Remarkably, the analysis findings of the two methods differ from each other. The findings reveal significant insights into the financial health and growth potential of clean energy companies, offering valuable guidance for future.

The Effects of Green Technology, Clean Energy, and Green Finance on Faith-Based Investments: Evidence from Islamic Stock Markets

This study examines the long-term relationship between Islamic Stock prices, faith-based investments, green technology, clean energy, and green finance. The variables used in the study are the Clean Energy Index for clean energy, Green Bond Index for green finance, the Renewable Energy and Clean Technology Index for green technology and the Islamic Market Index for Islamic Stocks’ closing prices, as explained by S&P. The utilized research model is the NARDL model, and the findings reveal a cointegration relationship between faith-based and green finance instruments. Green finance instruments are unsuitable for the research period to be used as a safe-haven or effective diversification instrument for faith-based portfolios. Comprehending faith-based investments’ relationship with other green finance assets is critical in supporting sustainable development with financial market instruments, which provide environmental and societal benefits. As a result, apprehending the relationship between faith-based investments and green finance instruments can contribute to increasing social and environmental responsibility awareness in financial markets. The study’s outcomes hold significance in understanding how investors in financial markets react to positive and negative shocks, estimating these effects, efficiently managing portfolios comprising green financial assets and faith-based investments, and formulating an appropriate investment strategy.

Mimicking crypto portfolios in sustainable investment

In this paper, considering the difference in the energy demand level, we utilize the daily pricing data from 10/01/2019 to 06/30/2023 to construct mimicking crypto portfolios with 12 clean cryptocurrencies to replace the dirty cryptocurrency, Bitcoin (BTC). With a monthly rebalancing strategy, the mimicking portfolio closely matches the exposures to the risk factors of the BTC but with fewer specific risks. Furthermore, relying on the bivariate dynamic conditional correlation (DCC-) GARCH model, we compare the hedging capability of BTC and the corresponding mimicking crypto portfolio against movements of returns of sustainable assets. The empirical results show that the mimicking crypto portfolio provides ESG investors with a cheaper hedge tool of higher hedge effectiveness compared to the BTC. Moreover, we find that the mimicking crypto portfolio can act as a strong safe haven for the S&P Global Clean Energy Index and S&P Latin America Emerging LargeMidCap ESG Index during periods of market stress. Therefore, the mimicking crypto portfolio is a more attractive option for ESG investors due to its superior hedging efficiency and the added environmental advantages.

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.

Exploring the Relationship between Clean Energy Indices and Oil Prices: a Ten-Day Window approach

This paper aims to assess the comovements between clean energy indices, namely the Clean Energy Fuels (CLNE), Nasdaq Clean Edge Green Energy (CELS), S&P Global Clean Energy (SPGTCLEN), TISDALE Clean Energy (TCEC.CN), Wilderhill (ECO), West Texas Intermediate (WTI) stock indices, over the period from 1 January 2018 to 23 November 2023. We used 10-day windows to analyse the duration and nature of the shocks. Granger causality tests revealed that 20 of the 30 possible pairs showed significant movements, with the WTI influencing all the clean energy indices, highlighting its global importance. CELS also showed a robust influence on all pairs, while SPGTCLEN had a significant but less far-reaching influence. The CLNE and ECO indices showed limited influences, suggesting the potential for diversification, the TCEC.CN proved to be independent and a determining factor for portfolio diversification. The Impulse Response Functions (IRF) confirmed significant movements between CELS, SPGTCLEN and WTI, reflecting the market's response to policies and adjustments in expectations. Fluctuations in oil prices substantially affect clean energy indices, highlighting the interconnectedness and volatility of these markets. In conclusion, these results indicate that despite the growth of clean energy, the sector is still influenced by fluctuations in the fossil fuel market.

Are clean energy markets hedges for stock markets? A tail quantile connectedness regression

Acknowledging the long-term potential of alternative energy sources, this paper examines the quantile frequency connectedness between clean energy markets and international stock markets, with implications related to hedging effectiveness. The main results point out that spillovers run from the US, the EU, the UK, and the Renewable Energy and Clean Technology Index to Japan and the Global Clean Energy Index. Furthermore, while the transmissions are concentrated in the short run during normal (0.5) and bull market (0.95) conditions, they extend to intermediate and long-term amid busting market (0,05 quantile) states, signifying a long-lasting impact that cannot be absorbed in the short run. Notably, clean energy index roles in information transmissions range from a net sender (Renewable Energy and Clean Technology Index), isolated (Green Bond Index), and a net receiver (Global Clean Energy Index). From a multivariant portfolio design perspective, we notice that a substantial weight should be allocated to clean energy assets, WTI, and CSI300 when compared with the rest of the financial markets. Moreover, the low (high) volatility regime yields lower (higher) weights than the ones reported in the mean state, but the results remain largely similar. Bivariant portfolio weights show that GB should have substantial weight when paired with all assets.

Clean energy market connectedness and investment strategies: New evidence from DCC-GARCH R[formula omitted] decomposed connectedness measures

In this study, we investigate the return propagation mechanism across four clean energy indices, namely, the NASDAQ OMX Green Economy Index, NASDAQ OMX Solar Energy Index, NASDAQ OMX Wind Energy Index, and NASDAQ OMX Geothermal Energy Index ranging from December 21st, 2010 until June 2nd, 2023 by using a novel DCC-GARCH-based R2 decomposed connectedness approach. This framework allows us to efficiently decompose dynamic conditional R2 goodness-of-fit measures into its decomposed components. Furthermore, we introduce the concept of minimum R2 decomposed connectedness portfolios and multivariate hedging portfolios. We find that the dynamic total connectedness is heterogeneous over time and economic-event dependent. In addition, the empirical results highlight that the NASDAQ OMX Green Economy Index is a net transmitter of shocks while all others are net receivers of shocks. Finally, we find that our proposed portfolio technique outperforms the NASDAQ OMX Green Economy Index as well as all alternative multivariate portfolio techniques regarding the hedging effectiveness score and the Sharpe ratio.

Analyzing Crisis Dynamics: How metal-energy Markets influence green electricity investments

The world continues to face major economic challenges due to crises that have a considerable impact on energy consumption. Since the global energy crisis, energy commodity costs have risen, and economic slowdowns in various regions continue to influence electricity market trends. Clean energy markets, propelled by the growing interest of economic entities and investors, are now interconnected with several commodity markets. In this context, energy metals play a crucial role in influencing the dynamics of the clean energy and particularly electricity markets. This study takes a close look at the complex interconnections between clean energy indices and the metals market, focusing on volatility dynamics. Drawing on advanced methodologies, including the Diebold and Yilmaz method, we unveil the complex relationships that link these markets. Our results reveal distinct patterns and interactions, highlighting the nuanced nature of the connection between clean energy indices and metal prices. The insights revealed have significant implications for policymakers and investors seeking to align their strategies with sustainable energy transitions and ensure financial stability. By enhancing our understanding of the interdependencies between clean energy indices and metals markets, this research provides valuable guidance for navigating the changing clean energy investment landscape.

Impact of Climate Policy Uncertainty, Clean Energy Index, and Carbon Emission Allowance Prices on Bitcoin Returns

This research aimed to investigate the relationship between climate policy uncertainty (CPU), clean energy (ENERGY), carbon emission allowance prices (CARBON), and Bitcoin returns (BTC) for the period from August 2012 to August 2022. The empirical analysis strategies utilized in this study included the Fourier Bootstrap ARDL long-term coefficient estimator, the Fourier Granger Causality, and the Fourier Toda–Yamamoto Causality methods. Following the confirmation of cointegration among the variables, we observed a positive relationship between BTC and CARBON, a positive relationship between BTC and CPU, and a negative relationship between BTC and ENERGY. In terms of causal associations, we identified one-way causality running from CARBON to BTC, BTC to CPU, and BTC to the ENERGY variable. The study underscores the potential benefits and revenue opportunities for investors seeking diversified investment strategies in light of climate change concerns. Furthermore, it suggests actionable strategies for policymakers, such as implementing carbon taxes and educational campaigns, to foster a transition towards clean energy sources within the cryptocurrency mining sector and thereby mitigate environmental impacts.

Climate policy uncertainty, media coverage of climate change, and energy markets: New evidence from time-varying causality analysis

Climate change is one of the most critical issues in the last decade, making investigating climate change risks' effects on the economy vital. Employing a novel time-varying Granger causality approach, we test causality from climate policy uncertainty (CPU) to the index returns of clean energy and nonrenewable energy sectors between November 2009 and December 2021. Our analysis generally reveals a significant causality from CPU to S&P clean energy sector index return throughout the sample period. In contrast, very limited significant causality is observed running from the CPU to the S&P nonrenewable energy index return. This result implies that investments in clean energy firms are affected by the CPU due to the cost of reversing the decisions in uncertain environments. On the other hand, the U.S. newspaper media coverage of climate change has a significant impact not only on the clean energy index returns but also on non-renewable energy index returns, implying an increase in the media coverage regarding climate change influences the awareness of investors on climate change, which affects their trading strategies.

Animal Manure as an Alternative Bioenergy Resource in Rural Sub-Saharan Africa: Present Insights, Challenges, and Prospects for Future Advancements

Energy availability is a pivotal driver in fostering sustainable socio-economic development. However, sub-Saharan Africa (SSA) grapples with paradoxes headlined by abundant energy resources but with the world’s lowest access to clean energy index per capita. Faced with a lack of access to clean energy sources like electricity, rural areas in the majority of SSA countries almost exclusively depend on biomass-fuels, mostly fuelwood, leading to heightened respiratory health risks as well as environmental degradation and accelerated climate change. As an alternative, this review investigates the potential of animal manure as a sustainable energy resource for rural SSA households, emphasising its utilisation as a feedstock for biogas production using anaerobic digestor technology. Results show that despite the abundance of literature that reports on successes in lab-scale bioreactor optimisation, as well as successes in the initial rollout of biogas biodigester technology in SSA with the help of international collaborators, the actual uptake of biogas bioreactor technology by rural communities remains low, while installed bioreactors are experiencing high failure rates. Resultantly, rural SSA still lags significantly behind in the adoption of sustainable clean energy systems in comparison to rural communities in other regions. Among some of the hurdles identified as driving low technology assimilation are onerous policy requirements, low-level government involvement, high bioreactor-instalment costs, the lack of training and awareness, and water scarcity. Prospects for success lie in innovative technologies like the low-cost portable FlexiBiogas system and private–public partnerships, as well as flexible energy policy frameworks. Bridging the knowledge-implementation gap requires a holistic approach considering cultural, technological, and policy aspects.

Do green investments improve portfolio diversification? Evidence from mean conditional value-at-risk optimization

This study investigates how green investment assets improve optimal portfolio diversification in terms of tail downside risk. We use the wavelet conditional value-at-risk ratio to explore the benefits of adding green assets to conventional portfolios. We quantify risk based on the contagion between conventional stock market indices and green environmental assets, including a sustainability index, clean energy, and green bonds. Our findings emphasize the high variance between conventional stock pairs, providing evidence of contagion effects before and during the COVID-19 pandemic. We show that including clean energy and green bond indices in conventional portfolios reduces the extreme risk of portfolios. In addition, we find that the diversification benefits of clean energy, green bonds, and safe-haven investments apply especially in the short term during the pandemic. Finally, we show that the considered portfolios could not decrease long-term risk during the COVID-19 crisis because of the systematic risk spread. Our portfolio optimization design supports the superiority of clean energy and green bonds in portfolio diversification over the sustainability index. These insights can be used by portfolio managers to inform diversification in different investment horizons.

The interconnected carbon, fossil fuels, and clean energy markets: Exploring Europe and China's perspectives on climate change

Carbon allowances represent a crucial financial instrument for mitigating the consequences of global warming and climate change. However, the feedback mechanism for Carbon-Energy- Finance systems introduces complexity due to the incorporation of equity, bond, and non- energy commodity assets. This study examines the volatility dynamics between carbon markets in Europe and China and their interactions with fossil fuel energy and clean energy indices. To measure this connectivity, we use advanced methodologies, such as the Diebold and Yilmaz method, to shed light on the complex relationships between these markets. Our study highlights significant disparities between China and Europe in terms of return spillover. In the short term, our results indicate that the Chinese carbon market acts as a receiver of shocks from other markets, while the European carbon market acts as a transmitter of shocks to various energy sources. In the specific contexts of China and Europe, these results provide valuable insights for policymakers and investors interested in tailoring approaches to sustainable energy transitions and financial stability.

The impact of conventional energy markets on connectedness dynamics among eco-friendly assets

ABSTRACT This study examines the connectedness among eco-friendly assets and how volatility in fossil energy markets affects this connectedness. Using a broad coverage for eco-friendly assets such as a clean energy index, a clean cryptocurrency (Cardano), a green bond index, and a sustainability-leaders index, our results show that the connectedness is higher in the extremities (tail-risk spillovers) and more significant during essential world events such as a pandemic or war. Further, we employ nonparametric causality in quantiles, quantile regression, and kernel-based regularised least squares methods to investigate the influence of fossil energy markets (crude oil, natural gas, and gas oil) volatility on eco-friendly assets’ connectedness. Our results imply that fossil energy market volatility has a positive and causal effect on connectedness dynamics among eco-friendly assets. Our study offers important implications for investors and policymakers in crafting portfolio strategies and policy decisions.

Dynamics Connectedness: TVP-VAR Insights into the Nexus between Selected Global Green Financial Instruments

Research aims: This study investigates the dynamics of connectedness among global green financial instruments driven by low-carbon policies. Design/Methodology/Approach: Employing the Time-Varying Parameter Vector Autoregressive (TVP-VAR) and Wavelet analysis, the researchers analyzed four primary variables derived from historical closing price indices, namely Global Carbon Efficiency Index (SPGCEI), Global Clean Energy Index (SPGCE), Global Green Bond Index (SPGBI), and Global Sukuk Index (SPGSI). Daily data from January 2, 2015, to November 8, 2023, was considered. Data processing was then carried out utilizing E-Views 13 and R-Studio. Research Findings: The findings demonstrated that a low-carbon policy stimulates green financing through the stock market and increases the bond and sukuk for carbon reduction. Moreover, the study revealed that the dynamic connectedness level of all variables was 45.25%. While the Global Carbon Efficiency Index (SPGCEI) and Green Bond Index (SPGBI) act as net pairwise transmitters, the Global Clean Energy Index (SPGCE) and Global Sukuk Index (SPGSI) function as net pairwise receivers. Theoretical Contribution/Originality: The study confirms that low-carbon policies drive green financing through stocks, promoting bond and sukuk activities for carbon reduction. By identifying the dynamic connectedness level and the roles of net transmitter and net receiver spillovers, it validates the impact of policies and introduces an innovative analytical framework for future research on the evolving dynamics of green finance. Policy Implication: The study recommends regulatory efforts to enhance connectedness and liquidity in green financial instruments to foster an effective and sustainable low-carbon ecosystem.