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PurposeThis study aims to examine the ability of clean energy stocks to provide cover for investors against market risks related to climate change and disturbances in the oil market.Design/methodology/approachThe study adopts the feasible quasi generalized least squares technique to estimate a predictive model based on Westerlund and Narayan's (2015) approach to evaluating the hedging effectiveness of clean energy stocks. The out-of-sample forecast evaluations of the oil risk-based and climate risk-based clean energy predictive models are explored using Clark and West's model (2007) and a modified Diebold & Mariano forecast evaluation test for nested and non-nested models, respectively.FindingsThe study finds ample evidence that clean energy stocks may hedge against oil market risks. This result is robust to alternative measures of oil risk and holds when applied to data from the COVID-19 pandemic. In contrast, the hedging effectiveness of clean energy against climate risks is limited to 4 of the 6 clean energy indices and restricted to climate risk measured with climate policy uncertainty.Originality/valueThe study contributes to the literature by providing extensive analysis of hedging effectiveness of several clean energy indices (global, the United States (US), Europe and Asia) and sectoral clean energy indices (solar and wind) against oil market and climate risks using various measures of oil risk (WTI (West Texas intermediate) and Brent volatility) and climate risk (climate policy uncertainty and energy and environmental regulation) as predictors. It also conducts forecast evaluations of the clean energy predictive models for nested and non-nested models.
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We investigate the determinants of clean energy stock returns by considering a large set of variables. We focus on the Covid-19 period and use a novel statistical technique, best subset regressions with non-Gaussian errors, for variable selection. Our examination shows that clean energy stocks are significantly exposed to small company and emerging market equities, a new finding to the literature. Moreover, we find no influence from the oil market, contrary to conclusions of a large part of the prior work.
ABSTRACT
The COVID-19 pandemic stimulated the need to invest in clean energy firms for better returns and climate risk mitigation. This study provides a detailed overview of the impact of idiosyncratic risk (IVOL) on excess returns of 95 clean energy stocks. Overall, investors in clean energy stocks are guided by the pessimist group of investors who underprice the high IVOL stocks and demand high-risk premiums to diversify the firm-specific risk. Further, during the COVID-19 period, there is no significant relationship between clean energy excess stock returns and IVOL. During this period, clean energy stocks were exposed to higher information asymmetry, limiting the arbitrage opportunities and producing a weaker return-IVOL relation indicating that clean energy stocks reflect the properties of technology stocks. IVOL has a low level of persistence which may be helpful in forecasting. This study offers valuable insights for regulators and investors from the investment decisions, asset pricing, and diversification perspective.
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In the backdrop of the recent covid-19 pandemic there is a renewed interest to understand the interlinkages between dirty and clean energies. In this regard, the study examines the co-movement structure between clean energy stocks and dirty energies before and during the covid-19 outbreak. The study analyses the interlinkages between the underlying markets by utilizing a vast sample of dirty energies namely crude oil, heating oil, gas oil, gasoline and natural gas, whereas clean energy sector is proxied by S&P Global clean energy index and Wilder Hill clean energy index. We make use of rolling window wavelet approach and wavelet coherence analysis to identify interdependencies between the clean energy stocks and dirty energies. The results exhibit weak linkages between clean energy equities and dirty energies in the short-run, while;we also record few occasions of high co-movements among dirty and clean energy markets in the long-run. Noticeably, a distinct decoupling effect persisted between dirty and clean energy markets. In addition, the findings also illustrate that clean energy market is relatively isolated from dirty energies during the recent pandemic crisis, amplifying portfolio diversification benefits across clean and dirty energy markets. The findings of the study hold meaningful insights for investors, policy makers and other market participants in energy financial markets.
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Purpose - This study seeks to investigate role of the coronavirus disease 2019 (COVID-19) pandemic on clean energy stocks for the United States for the period 21 January 2020-16 August 2021. Design/methodology/approach - At the empirical stage, the Fourier-augmented vector autoregression approach has been used. Findings - According to the empirical results, the response of the clean energy stocks to the feverish sentiment, lockdown stringency, oil volatility, dirty assets, and monetary policy dies out within a short period of time. In addition, the authors find that there is a unidirectional causality from the feverish sentiment index and the lockdown stringency index to the clean energy stock returns;and from the monetary policy to the clean energy stocks. At the same time, there is a bidirectional causality between the lockdown stringency index and the feverish sentiment index. The empirical findings can be helpful to both practitioners and policy-makers. Originality/value - Among the COVID-19 variables used in this study is a new feverish sentiment index, which has been constructed using principal component analysis. The importance of the feverish sentiment index is that it allows us to examine the impact of the aggregate level of fear in the economy on clean energy stocks.
ABSTRACT
The COVID-19 pandemic stimulated the need to invest in clean energy firms for better returns and climate risk mitigation. This study provides a detailed overview of the impact of idiosyncratic risk (IVOL) on excess returns of 95 clean energy stocks. Overall, investors in clean energy stocks are guided by the pessimist group of investors who underprice the high IVOL stocks and demand high-risk premiums to diversify the firm-specific risk. Further, during the COVID-19 period, there is no significant relationship between clean energy excess stock returns and IVOL. During this period, clean energy stocks were exposed to higher information asymmetry, limiting the arbitrage opportunities and producing a weaker return-IVOL relation indicating that clean energy stocks reflect the properties of technology stocks. IVOL has a low level of persistence which may be helpful in forecasting. This study offers valuable insights for regulators and investors from the investment decisions, asset pricing, and diversification perspective.
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Combining advanced quantile-on-quantile (QQ) regression and causality-in-quantiles (QC) methods, we examine the asymmetric effects of non-ferrous metal price shocks on clean energy stocks at aggregate and sub-sector levels. From the aggregate perspective, the impact of non-ferrous metal price shocks is strongly negative for bull clean energy stock markets but is positive under bear circumstances. According to QC analysis, non-ferrous metal price shocks can effectively predict returns on clean energy stocks in some quantiles. Sub-sectors of clean energy stocks react differently, proving the heterogeneity of different industries. Synergistic movements between non-ferrous metal price shocks and some clean energy sectors in bear markets are detected, indicating that non-ferrous metals are not safe havens for clean energy stock markets under extreme market conditions. Furthermore, non-ferrous metals have a significantly stronger negative impact on clean energy stocks during the epidemic, demonstrating the structural changes effect of COVID-19.
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Given the recent evolution of green bonds as hedging tool on the face of climate change and green energy transitions, as well as cryptocurrencies’ popularity as portfolio diversifier, prior literature could focus on the potential impacts of environmental concerns in conjunction with cryptocurrencies on the performance of green financial assets. Against this backdrop, we analyse the impact of cryptocurrency environment attention index (ICEA) on clean energy stocks and green bonds using a range of econometric methods. Specifically, we use OLS, and quantile-based regression, quantile connectedness approach, and dynamic conditional correlations (DCC)-GJR-GARCH model to analyse the data. Quantile regression results suggest that ICEA exerts positive effects on S&P500 stocks in bearish market conditions and on water stocks in normal to bullish market conditions. Interestingly, clean energy stocks and green bonds have insignificant relationship with the ICEA based on OLS and quantile regression results. While, quantile connectedness results show that connectedness among the assets is low (high) at lower (higher) quantiles. Additionally, ICEA transmits (receives) weak spillovers to (from) other assets at lower quantiles, thus there is potential for diversification with clean energy stocks and green bonds in the portfolio against ICEA in bearish market conditions. Our DCC – GARCH based analysis shows that gold has positive relationship with the ICEA. DCCs also show that clean energy stocks have consistently positive relationship with ICEA, specifically during the period of high spikes of ICEA in 2017–2021, but green bonds failed to maintain consistent positive correlations with ICEA during such period. Finally, covid period reveals higher connectedness and changes in direction of contagion among assets, and lack of significant relationship between ICEA and asset returns. Our findings have important implications for the investors in the construction of optimal portfolio with carbon free assets in different markets states.
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We examine market integration across and clean and green investments, crude oil, and conventional stock indices covering technology stocks, and United States and European stocks. Using daily data covering the period December 1, 2008-October 8, 2020, we first apply the dynamic equicorrelation (DECO) model and make inferences regarding the time-varying level of market integration. Then, we use several regression models and uncover the driving factors of market integration under lower and upper quantiles of the distribution of the equicorrelation. The results show that return equicorrelation varies with time and is shaped by the COVID19 outbreak. Various uncertainty measures are the main drivers of market integration, especially at high levels of market integration. During the COVID-19 outbreak period, the United States Dollar index, the term spread, and the Chinese stock market index have significantly increased market integration.
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This study examines the diversification and hedging benefits of green investments for conventional stock portfolios in the context of the recent COVID-19 pandemic. While the findings confirm the status of gold as a strong hedge against stock market downturns, we find that clean energy investments, green bonds, in particular, have the potential to serve as a safe haven as well. In fact, compared to the other alternative and sustainable investments in our sample, green bonds are found to be the only asset that serves as a safe haven against large stock market fluctuations due to the COVID-19 pandemic. Portfolio analysis further shows that supplementing conventional stock portfolios with green bonds during the COVID-19 pandemic resulted in the highest risk-adjusted returns, compared to those supplemented with other alternative assets in the sample. Our findings support the emergence of green investments not as a luxury good, but a necessity for improved financial stability and performance, particularly during the turbulent market states driven by the recent pandemic. © 2021 Elsevier B.V.
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This study examines the return and volatility connectedness between the rare earth stock market and clean energy markets, world equity, base metals, gold, and crude oil. Using daily data from September 21, 2010 to August 28, 2020, a time-varying parameter vector autoregression (TVP-VAR) approach to connectedness is applied to uncover the dynamics of connectedness during the entire period and the COVID-19 pandemic period. Volatility connectedness is generally stronger than return connectedness. However, the return and volatility connectedness pattern varies over the full sample period, exhibiting a significant spike following the abrupt COVID-19 outbreak in February-March 2020. The rare earth index shows a close interdependence with the clean energy, world equity, and oil indexes during the outbreak of the pandemic, though it mostly remains a return and volatility receiver over the entire period. During the COVID-19 outbreak, the rare earth stock index becomes more central to the network of connectedness for both return and volatility, showing strong interdependence with clean energy and world equity. The volatility of the rare earth stock index exhibits a strong interdependence with that of crude oil prices. Our findings help investors understand diversification benefits and investment protection. They support policymakers in developing strategies for lessening import dependence on rare earth metals.