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This study aims to examine the hedge and safe-haven properties of the Sukuk and green bond for the stock markets pre- and during the COVID-19 pandemic period.

To test the hedge and safe-haven characteristics of Sukuk and green bonds for stock markets, the study first uses the methodology proposed by Ratner and Chiu (2013). Next, the authors estimate the hedge ratios and hedge effectiveness of using Sukuk and green bonds in a portfolio with stock markets.

Strong safe-haven features of ethical (green) bonds reveal that adding green bonds into the investment portfolios brings considerable diversification avenues for the investors who tend to take fewer risks in periods of economic stress and turbulence. The hedge ratio and hedge effectiveness estimates reveal that green bonds provide sufficient evidence of the hedge effectiveness for various international stocks.

The study has significant implications for faith-based investors, ethical investors, policymakers and regulatory bodies. Religious investors can invest in Sukuk to relish low-risk and interest-free investments, whereas green investors can satisfy their socially responsible motives by investing in these investment streams. Policymakers can direct the businesses to include these diversifiers for portfolio and risk management.

The study provides novel insights in the testing hedge and safe-haven attributes of green bonds and Sukuk while using unique methodologies to identify multiple low-risk investors for investors following the uncertain COVID-19 pandemic.

This study adopts a new macro-perspective to explore the complex and dynamic links between financial instability and the Euro-American green equity market. Its primary focus and novelty is to shed light on the non-linear and asymmetric characteristics of dependence, causality, and contagion within various time and frequency domains. Specifically, the authors scrutinize how financial instability in the U.S. and EU interacts with their respective green stock markets, while also examining the cross-impact on each other's green equity markets. The analysis is carried out over short-, medium- and long-term horizons and under different market conditions, ranging from bearish and normal to bullish.

This study breaks new ground by employing a model-free and non-parametric approach to examine the relationship between the instability of the global financial system and the green equity market performance in the U.S. and EU. This study's methodology offers new insights into the time- and frequency-varying relationship, using wavelet coherence supplemented with quantile causality and quantile-on-quantile regression analyses. This advanced approach unveils non-linear and asymmetric causal links and characterizes their signs, effectively distinguishing between bearish, normal, and bullish market conditions, as well as short-, medium- and long-term horizons.

This study's findings reveal that financial instability has a strong negative impact on the green stock market over the medium to long term, in bullish market conditions and in times of economic and extra-economic turbulence. This implies that green stocks cannot be an effective hedge against systemic financial risk during periods of turbulence and euphoria. Moreover, the authors demonstrate that U.S. financial instability not only affects the U.S. green equity market, but also has significant spillover effects on the EU market and vice versa, indicating the existence of a Euro-American contagion mechanism. Interestingly, this study's results also reveal a positive correlation between financial instability and green equity market performance under normal market conditions, suggesting a possible feedback loop effect.

This study represents pioneering work in exploring the non-linear and asymmetric connections between financial instability and the Euro-American stock markets. Notably, it discerns how these interactions vary over the short, medium, and long term and under different market conditions, including bearish, normal, and bullish states. Understanding these characteristics is instrumental in shaping effective policies to achieve the Sustainable Development Goals (SDGs), including access to clean, affordable energy (SDG 7), and to preserve the stability of the international financial system.

This paper aims to explore the interaction between investor attention and green security markets, including green bonds and stocks.

This study takes the Baidu index of “green finance” as the proxy for investor attention and constructs several generalized prediction error variance decomposition models to investigate the interdependence. It further analyzes the dynamic interaction between investor attention and the return and volatility of green security markets using the rolling time window.

The empirical analysis and robustness test results reveal that the spillovers between investor attention and the return and volatility of the green bond market are relatively stable. In contrast, the spillover level between investor attention and the green stock market displays significant time-varying and asymmetric effects. Moreover, the volatility spillover between investor attention and green securities is vulnerable to major financial events, while the return spillover is extremely sensitive to market performance.

The conclusion further expands the practical application and theoretical framework of behavioral finance in green finance and provides a new reference for investors and regulators. Besides, this study also lays a theoretical basis for investors to focus on the practical application of volatility prediction and risk management in green securities.

This paper aims to examine the comovement among green bonds, energy commodities and stock market to determine the advantages of adding green bonds to a diversified portfolio.

Generic 1 Natural Gas and Energy Select SPDR Fund are used as proxies to measure energy commodities, bonds index of S&P Dow Jones and Bloomberg Barclays MSCI are used to represent green bonds and the New York Stock Exchange is considered to measure the stock market. Granger causality test, wavelet analysis and network analysis are applied to daily price for the select markets from August 26, 2014, to March 30, 2021.

Results from the Granger causality test indicate no causality between any pair of variables, while cross wavelet transform and wavelet coherence analysis confirm strong coherence at a high scale during the pandemic, validating comovement among the three asset classes. In addition, network analysis further corroborates this connectedness, implying a strong association of the stock market with the energy commodity market.

This study offers new evidence of the temporal association among the US stock market, energy commodities and green bonds during the COVID-19 crisis. It presents a novel approach that measures and evaluates comovement among the constituent series, simultaneously using both wavelet and network analysis.

The purpose of this paper is to explore the source of apparent abnormal returns accrued by “green” company stocks. Though one cannot completely rule out that market-to-book and size factors may already capture the information of Trucosts’ total damage measure, the authors attempt to attribute the effect to risk, a persistent desirable characteristic or a short-run attention effect.

The authors construct portfolios of stocks using the Trucost data for identifying more environmentally friendly companies. The authors then compare the risk-adjusted returns of the green portfolios to the non-green portfolios. A secondary analysis of the price impact of being listed on the Newsweek green company listed is used to determine attention effects.

The authors find that green stock returns outperform the most polluting stocks by 3.7 percent per year on a risk-adjusted basis. The evidence is most consistent with a significant but economically small attention effect coupled with a longer lasting and greater magnitude desirable characteristic driving green returns. The authors do not find evidence of a risk-contribution to the performance after controlling for well-known factors.

Fund managers may benefit from this research in selecting green stocks, and thereby enhancing investment performance, with desirable characteristics without fear of increasing risk.

One social implication is that investing in sustainable and green firms may not only be beneficial for the common good but also for the investor. Increased capital flows, and hence lower borrowing costs, for green firms may assist in creating a more ecologically sustainable economy.

To the authors’ knowledge this paper unique in attempting to determine if the green premium is a short-run inefficiency resolved by attention or a result of a desirable characteristic.

Increasing focus on socially responsible investments (SRIs) and green projects in recent times, coupled with the arrival of COVID pandemic, are the main drivers of this study. The authors conduct a post-factum analysis of investor choice between sin and green investments before and through the COVID outbreak.

A passive investor is introduced who seeks maximum risk-adjusted return and/or investment variance. When presented an opportunity to add sin and/or green investments to her initial one-asset market-only investment position, she views and handles this issue as a portfolio problem (MPT). She estimates value-at-risk (VaR) and conditional-value-at-risk (CVaR) for portfolios to account for downside risk.

Green investments offer better overall risk-return optimization in spite of major inter-period differences in return-risk dynamics and substantial downside risk. Portfolios optimized for minimum variance perform just as well as the ones optimized for minimum downside risk. Return and risk have settled at higher levels since the onset of COVID, resulting in shifting the efficient frontier towards north-east in the return-risk space.

The study contributes to the literature in two ways: One, it examines investor choice between sin and green investments during a global health emergency and views this choice against the one made during normal times. Two, instead of using the principles of modern portfolio theory (MPT) explicitly for diversification, the study uses them to identify investor preference for one over the other investment type. This has not been widely done thus far.

The objective of this paper is to investigate the impact of the information sharing of the dynamic demand on green technology innovation and profits in supply chain from a long-term perspective.

The authors consider a supply chain consisting of a manufacturer and a retailer. The retailer has access to the information of dynamic demand of the green product, whereas the manufacturer invests in green technology innovation. Differential game theory is adopted to establish three models under three different scenarios, namely (1) decentralized decision without information sharing of dynamic demand (Model N-D), (2) decentralized decision with information sharing of dynamic demand (Model S-D) and (3) centralized decision with information sharing of dynamic demand (Model S-C).

The optimal equilibrium results show that information sharing of dynamic demand can improve the green technology innovation level and increase the green technology stocks only in centralized supply chain. In the long term, the information sharing of dynamic demand can make the retailer more profitable. If the influence of green technology innovation on green technology stocks is great enough or the cost coefficient of green technology innovation is small enough, the manufacturer and decentralized supply chain can benefit from information sharing. In centralized supply chain, the value of demand information sharing is greater than that of decentralized supply chain.

The authors used game theory to investigate demand information sharing and the green technology innovation in a supply chain. Specially, the demand information is dynamic, which is a variable that changes over time. Moreover, our research is based on a long-term perspective. Thus, differential game is adopted in this paper.

The purpose of this paper is to investigate green exchange‐traded funds (ETFs) and propose a market‐wide proxy for green returns and a green volatility factor.

Identifying a unique sample of green funds, this paper investigates the time‐series behavior of returns for these investment vehicles and their associated conditional volatility dynamics via GARCH methodology. In this study, green ETFs are defined as index funds replicating market indices that invest in stocks exhibiting positive environmental, social, and governance characteristics.

Cumulative market‐wide green returns are found to be positive from inception year 2005 through 2008. Estimating a t‐GARCH (1,1) specification, the author finds strong evidence in favor of volatility persistence for the 15 green ETFs identified in this study. Additionally, the results suggest that a 1 percent volatility‐based value‐at‐risk forecast ranges from $24,150 through $26,000 on a daily basis. Finally, the empirical evidence provides support for weak‐form market efficiency when examining the green universe of stocks.

Green ETFs are relatively recent financial instruments and exhibit important implications for volatility timing strategies and the cross‐section of green stock returns.

Knowledge of green price behavior is important in constructing optimal hedging and risk management strategies.

Green ETFs provide a natural channel for sustainable investing.

Green ETFs exhibit the advantage of including many companies that have undertaken positive measures towards the global environment in their respective business models. This paper provides the first investigation of green ETFs.

This study aims to examine the impact of green operation (measured using the energy intensity of its operations) on the value of corporate firms in stock markets. The authors also examine the channel of such an impact and its implication on a firm's financing choices.

The authors conduct various univariate and multivariate regression analyses on a panel of all non-financial Indian firms listed on the National Stock Exchange from 2010 through 2018. The authors use the sensitivity of investments to the cash flows model to test the financial constraints hypothesis.

The authors’ analysis shows a positive relationship between energy efficiency (firms that consume a lesser amount of energy per unit of sale) and the value of firms in the stock market. The authors empirically attribute this greater valuation to the lesser volatility of stock returns, measured by the standard deviation of daily stock returns. Finally, the authors observe that investments in energy-efficient firms are less sensitive to their internal cash flows.

The results suggest that less green firms face greater constraints in accessing finance from external sources and, therefore, depend more on internal than external capital to finance their investments. Hence, managers of such firms can ease their financing pressures by making their operations greener.

In this study, the authors examine the implications of green operations on the financing choices of firms. This aspect of going green is important because managers will have enough incentives to invest in green technologies as that would increase their access to external finance and, hence, decrease their financial constraints.

This study aims to investigate the response of green investments of emerging countries to own-market uncertainty, oil-market uncertainty and COVID-19 effect/geo-political risks (GPRs), using the tail risks of corresponding markets as measures of uncertainty.

This study employs Westerlund and Narayan (2015) (WN)-type distributed lag model that simultaneously accounts for persistence, endogeneity and conditional heteroscedasticity, within a single model framework. The tail risks are obtained using conditional standard deviation of the residuals from an asymmetric autoregressive moving average – ARMA(1,1) – generalized autoregressive conditional heteroscedasticity – GARCH(1,1) model framework with Gaussian innovation. For out-of-sample forecast evaluation, the study employs root mean square error (RMSE), and Clark and West (2007) (CW) test for pairwise comparison of nested models, under three forecast horizons; providing statistical justification for incorporating oil tail risks and COVID-19 effects or GPRs in the predictive model.

Green returns responds significantly to own-market uncertainty (mostly positively), oil-market uncertainty (mostly positively) as well as the COVID-19 effect (mostly negatively), with some evidence of hedging potential against uncertainties that are external to the green investments market. Also, incorporating external uncertainties improves the in-sample predictability and out-of-sample forecasts, and yields some economic gains.

This study contributes originally to the green market-uncertainty literature in four ways. First, it generates daily tail risks (a more realistic measure of uncertainty) for emerging countries’ green returns and global oil prices. Second, it employs WN-type distributed lag model that is well suited to account for conditional heteroscedasticity, endogeneity and persistence effects; which characterizes financial series. Third, it presents both in-sample predictability and out-of-sample forecast performances. Fourth, it provides the economic gains of incorporating own-market, oil-market and COVID-19 uncertainty.