North American Journal of Economics and Finance最新文献

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.

We investigate the extreme risk contagion of hedge funds and mutual funds, thereby comparing their performance, and studying whether hedge funds are smarter than mutual funds or vice versa. We construct the Copula-CoVaR model to measure the dynamic and nonlinear extreme risk contagion of hedge funds and mutual funds from January 1994 to April 2020. Our findings suggest that compared with mutual funds, hedge funds are subject to lower extreme risk contagion and offer higher average returns. This outperformance is robust in different crisis periods. Moreover, although almost all types of hedge funds outperform mutual funds, the characteristics of hedge funds have a different impact on the outperformance. Specifically, hedge funds adopting strategies of long/short equity hedge, event-driven, funds of funds, emerging markets, equity market neutral, multi-strategy, and global macro, funds with moderate activism, and illiquid hedge funds are smarter than mutual funds from the perspective of extreme risk contagion.

In a market characterized by partial information, we delve into the influence of overconfidence on individual optimal consumption and portfolio decisions. To address this, we tackle the max–min expected utility problem, which allows us to derive the optimal consumption and portfolio rules. Solving this problem yields two higher-order nonlinear partial differential equations that capture the scaled deterministic equivalent wealth − a key component for evaluating the value function and quantifying welfare loss. This paper presents an alternative theoretical perspective on the phenomena of underconsumption or overinvestment, attributing these behaviors to the overconfidence bias. Our model forecasts that overconfidence bias leads to an excessive allocation to risky assets and a reduction in consumption, thereby inevitably resulting in a certain degree of welfare loss. Moreover, it provides a cohesive theoretical framework to explain stock return puzzles, such as the momentum and reversal effects, within a structured model. Significantly, we discover that the conditional Sharpe ratio adheres to a mean-reverting process. These insights indicate that overconfidence bias significantly influences individual behavior, which in turn has a profound impact on return anomalies.

The price-bubble and crash formation process is theoretically investigated in a two-asset equilibrium model. Sufficient and necessary conditions are derived for the existence of average equilibrium price dynamics of different agent-based models, where agents are distinguished in terms of factor and investment trading strategies. In line with experimental results, we show that assets with a positive average dividend, i.e., with a strictly declining fundamental value, display at the equilibrium price the typical hump-shaped bubble observed in experimental asset markets. Moreover, a misvaluation effect is observed in the asset with a constant fundamental value, triggered by the other asset that displays the price bubble shape when a sharp price decline is exhibited at the end of the market.

This study uses a time-varying parameter vector autoregression (TVP-VAR) model to examine the dynamic relationship between rating changes and portfolio returns in the US and Canada across the environmental (E), social (S), governance (G) and total ESG assessment pillars. The analysis includes both return and volatility spillovers and covers the period from March 2009 to October 2022. The study reveals a fluctuating pattern of connectedness, influenced by global financial events, such as the 2008 financial crisis. In particular, the US shows higher levels of connectedness. Rating changes, particularly in the ESG dimension, show stronger spillovers than returns, highlighting their importance in portfolio construction. The study further explores net connectedness profiles, identifying ESG rating changes as net transmitters. The results suggest that investors should prioritize rating changes over returns, highlighting the importance of considering ESG factors in portfolio management, especially the social criterion, to mitigate investment risks. The research contributes to the understanding of ESG dynamics in international equity markets and provides valuable insights for investors and market regulators.