North American Journal of Economics and Finance最新文献

Using a sample of CSI300 over the 2006–2021 period to establish liquidity spillover networks, we find a significantly negative relationship between liquidity connectedness and stock price crash risk. Further analysis shows that liquidity connectedness depresses stock price crash risk through two potential channels: increased conditional conservatism and decreased stock price synchronicity. Moreover, this effect is more prominent for firms with effective external monitoring, firms with lower risk-taking, and state-owned enterprises (SOEs). Overall, our paper shows that liquidity connectedness is an important factor influencing crash risk and provides useful guidance for corporate management and investor decision-making.

The study examines the link between bank competition and firms’ capital investment efficiency. Utilizing a unique dataset comprising Vietnamese listed firms from 2007 to 2022, we suggest that heightened bank competition, as reflected by lower values of concentration ratios, the Lerner index, and the Boone indicator, raises firms’ investment efficiency. Further analysis reveals that bank competition increases investment efficiency specifically in the form of mitigating the underinvestment issue. The validity of the result holds through numerous robustness tests, especially with careful consideration of endogeneity concerns. Through mechanism tests, our study reveals that increased bank competition elevates corporate investment efficiency by mitigating firms’ financing constraints, offering more bank credit, and reducing financing costs. In cross-sectional analysis, we document that the relationship between bank competition and capital investment efficiency is stronger for firms with closer bank-firm ties, greater investment opportunities, and weaker financial positions (captured by firm size, state ownership, and listing location). Further, we observe that the influence of bank competition is attenuated during macroeconomic shocks, as exemplified by the financial crisis and the coronavirus pandemic.

The behavior of institutional investors such as public offering funds and investor networks play an important role in information transmission and risk contagion in the capital market. Less attention has been paid to the macro topological structure characteristics and the fund group behavior of the co-holding network indirectly formed by the common holding among funds. Based on the complex network analysis method, this paper firstly uses three methods to define the co-holding behavior of funds and construct the co-holding networks between large funds and small funds and between large funds, and then conducts a comparative study on the Macro topology structure and evolution characteristics of the Chinese Public Funds’ Co-holding network. The results show that: (1) Although the three networks are large sparse networks, the co-holding behavior among funds still widely exists; (2) Both networks have the characteristics of small-world and scale-free, but there are significant differences in the degree of specificity; (3)There are significant differences in the evolution of “small-world and scale-free” between the three networks; (4) When the large funds and small funds are considered comprehensively, the “small world” and “scale-free” of the fund co-holding network and the stock market show a relationship of mutual influence and mutual restriction.This study provides a reference for understanding the influence of mutual shareholding among funds, and for regulators to manage stock market risk and institutional investor governance.

We employ a time-varying parameter vector autoregression (TVP-VAR) joint connectedness approach to study the dynamic risk spillover effects between cryptocurrencies and China’s financial market, further exploring the impact of cryptocurrencies on China’s financial market. Our results show that there is asymmetric risk transmission between cryptocurrencies and China’s financial market, and the risk spillover effect is very weak. Specifically, the spillover of cryptocurrencies to China’s financial market is significantly stronger than the spillover of China’s financial market to cryptocurrencies. Cryptocurrencies have a stronger spillover effect to China’s exchange rate and gold. The net spillover effect of cryptocurrencies is weakening over time. Overall, the return spillover impact of cryptocurrencies on China’s financial market is greater than the volatility spillover impact, and the degree of impact of different cryptocurrencies is heterogeneous. The findings of this study have several implications for policymakers and investors.

This study applies the Quantile-on-Quantile Connectedness approach to analyze quantile spillovers between the US yield curve spread (10-year vs. 2-year Treasury yields), the US dollar, and gold price from 2 January 2000 to 31 July 2023, covering the COVID-19 pandemic. Our results show that inversely related quantiles demonstrate significantly higher average total connectedness than directly related quantiles among these variables. Additionally, we found that this quantile-based connectedness fluctuates over time, suggesting a dynamic and varied relationship between the US yield spread, the US dollar, and gold prices throughout the period studied.

This paper examines a novel pattern of option return predictability. Specifically, we find option trading volume negatively and significantly predicts the cross-section of delta-hedged option returns. Our portfolio strategies of option trading volume yield significant returns in options across different moneyness and time to maturity. Furthermore, the evidence shows that market capitalization and idiosyncratic volatility are able to explain the predictability of option trading volume on option returns. Our results are robust to alternative measures of option returns and option subsamples.

This study analyses the portfolio balance channel of the U.S. quantitative easing (QE) by assessing the dynamic spillover effect between commodities and financial assets in commodity-exporting countries during QE. This study integrates the generalized spillover index initially proposed by Diebold and Yilmaz (2012) for the fractional integration VAR model. Then, we estimate the multivariate framework of the Westerlund and Narayan (2015) (WN)-based predictive model to quantify the effect of the portfolio balance channel on the net pairwise spillover index from the U.S. to other countries. Our results show: first, for bond yields, that Asian and Pacific bond yields are impacted by both commodity price indices returns and the U.S. bond yields across the sample periods. However, mixed evidence is found for both Latin America and Others; second, for equity, dynamic net return spillovers contribute mixed evidence across regional groups during QE. The diverse results are partly explained by the average percentage of commodity exports to total exports of the country and the degree of close interrelationship between countries. Additionally, dynamic return spillover analyses show that most foreign exchange returns are negative net spillovers during QE, supporting the behavior of “commodity currencies.” Last, the WN-based predictability models show pronounced differences in predictability across the selected commodity-exporting countries.

In this study, a Copula-MIDAS-TRV model with high-frequency realized volatility as the threshold variable is developed for the first time to fit the joint distribution of returns, which takes into account the impact of the leverage effect of volatility on the time-varying interdependence structure among financial markets. Based on this model, we empirically analyze the risk spillover effects between the CSI 300 index and the SSE Composite Index in the Chinese market and test the validity of the model in risk spillover measurement. The empirical findings demonstrate how well the Copula-MIDAS-TRV model, which is the focus of this work, can assess risk spillover effects and analyze the time-varying interdependence between these two indices.

Green finance (GF) plays a key role in combating climate change and advancing sustainable economic development. At the same time, governments have enacted various policies to pursue climate action and economic stability simultaneously, resulting in economic policy uncertainty (EPU) and climate policy uncertainty (CPU). While the EPU and CPU may have an impact on the GF market, they may also interact with each other and the impact of this interaction has received little attention. Therefore, it becomes crucial to understand how EPU and CPU affect GF. This paper explores the relationships among green bonds (GB), green stocks (GS), EPU and CPU in the context of China. Firstly, the nonparametric quantile causality test reveals the existence of causality in EPU/CPU-GB, EPU/CPU-GS, EPU-CPU, and GB-GS. The cross-quantilogram test result indicates that the negative predictive effects of EPU and CPU on the GF market are mainly concentrated at the extreme quantiles and an interaction exists between CPU and EPU. In addition, a negative correlation between the GB and GS markets is found in the short term suggesting that investors may achieve hedging risk and/or portfolio diversification if investing in these two green financial assets. The findings shed light for policymakers and relevant investors on how EPU and CPU shocks affect GF, and provide ideas on how to effectively hedge (deal with) these shocks in the asset allocation (policy making) process, thereby enhancing the development of the GF market.

We examine the effect of the US yield curve on the global green bond markets at the forefront of the climate change fight. For this purpose, we compute three components (level, slope, and curvature) of the US yield curve based on daily data of the treasury yields with several maturities from January 2009 to June 2022 and employ country-level S&P green bond indices. Our dynamic network analysis shows that the level component of the yield curve is more influential in transmitting return and volatility shocks to green bonds, while curvature is primarily absorptive. The European, the US, and Hong Kong green bonds are the leading players in shock propagation. Both return- and volatility spillovers are time-varying and remain high during periods of systemically important events, especially COVID-19 and the Russia-Ukraine war, supporting the Global Financial Cycle Hypothesis. The war also changes the net behaviors (transmitter versus receiver) of the components and the indices. Investors and issuers of green bonds are advised to keenly observe the shape of the US yield curve and systemic events for better decision-making regarding investment horizons and contagion risk management.