This study explores the role of green bonds in mitigating risks during extreme conditions, considering China and the US’ robust green bond markets and global risk events. It analyzes the interconnectedness of green bonds with other sectors like conventional bonds, equities, crude oil, and monetary policy. Using the quantile connectedness approach, it reveals how stabilized green bond markets in both countries act as hedges during extreme situations. By examining time-varying spillover effects, it identifies commonalities and differences in linkages between green bond markets and other sectors. These findings endorse green bonds’ integration into finance and hold implications for enhancing portfolio management and risk models.
This study focuses to examine the connectedness among the Green Investments, NFTs, DeFis & Green Cryptocurrencies, along with the portfolio diversification and hedging potential of the Green Investment against the other investments. We examined the connectedness using the Quantile VAR and Wavelet Quantile Correlation method, indicating the existence of the partial connectedness among the selected assets. The connectedness among the assets changes due to change in global uncertainty caused by Covid-19 and Russia-Ukraine war. The green investment offers the hedging benefits to other green investment. Among all crypto assets, Dai serve as a good hedge for the green investment and other crypto assets. MCoP is best performing portfolio with Sharpe ratio, followed by MCP. However, the investment as per MCoP and MCP approaches increases the volatility of green assets. Further, the hedging benefits are varying with the changing global dynamics. None of the approach gives positive cumulative return and Sharpe ratio to the investors during the Russia-Ukraine war period. Our study has implications for the investors and portfolio managers with respect to portfolio framing and fund allocation.
We analyze ESG-based investments in stocks across 23 developed markets using daily data from 2004 to 2022. The findings suggest a weak relationship between the ESG ratings and expected returns, with some evidence of modest underperformance of high ESG stocks compared to lower-rated ones in specific periods. This outcome indicates that stock prices have already reflected ESG information, and well-known asset pricing factors can effectively capture the returns of portfolios based on ESG ratings. However, the strength of this relationship depends on global attention to sustainability, where high ESG-rated stocks tend to gain advantages during unexpected attention increases, highlighting the dynamic, nonlinear nature of this relationship.
Utilizing a dataset from 2013 to 2022 on China’s listed companies, we explored whether a hedge fund network could help explain the occurrence of Chinese stock crash. First, this study constructs a hedge fund network based on common holdings. Then, from the perspective of network centrality, we explore the impact of hedge fund network on stock crash risk and its mechanisms. Our findings suggest that companies with greater network centrality experience lower stock crash risk. Such results remain valid after alternating measures, using the propensity score matching method, and excluding other network effects. We further document that the centrality of hedge fund network reduces crash risk through two channels: information asymmetry and governance monitoring. In addition, the negative impact of hedge fund network centrality on stock crash risk is more pronounced for non-SOEs firms. In summary, our research shed light on the important role of hedge fund information network in curbing stock crash.