This paper examines biodiversity risk transmission to US agricultural futures markets. We find: (1) all futures exhibit moderate-to-high biodiversity sensitivity, with coffee showing highest response through transparent price transmission mechanisms; (2) wavelet analysis reveals time-frequency heterogeneity, where tropical crops maintain strong long-term synchronization with biodiversity risk, intensified during COVID-19; (3) frequency-dependent asymmetric correlations emerge, with grains shifting from positive long-cycle to negative short-cycle correlations; (4) systemic spillover analysis indicates moderate interdependence, with soybeans as primary risk receiver and sugar as dominant transmitter, revealing differentiated transmission roles.

This study investigates the impact of Total Factor Productivity(TFP) growth in China's high-tech industry on household consumption, examining the distinct roles of labor and capital factor productivity from the perspective of biased technological progress. We innovatively construct a sequential proportional NDDF-Luenberger index. This index not only provides a theoretically consistent measure of TFP but also enables its precise decomposition into labor factor productivity and capital factor productivity, allowing for the quantitative identification of the degree and direction of technological bias. Our analysis yields three key findings. First, China's high-tech industry TFP evolved through a three-phase pattern of "surge–retreat–recovery," characterized by persistent capital-biased technological progress. Second, at the national level, improvements in overall TFP, labor factor productivity, and capital factor productivity all significantly promote household consumption, validating the theoretical pathway where supply-side efficiency gains stimulate demand. Third, significant regional heterogeneity exists: the Eastern region exhibits a "capital-led" growth pattern with weaker consumption effects from labor productivity; the Central and Western regions show "factor synergy," where both productivities contribute to consumption; whereas the Northeastern region suffers from a blocked transmission mechanism, where technological progress fails to significantly boost local consumption due to insufficient integration with the regional economy. By integrating supply-side TFP with demand-side consumption through the lens of biased technological progress, this research provides critical insights for fostering a virtuous cycle between innovation and domestic demand, offering valuable implications for industrial and regional policy design aimed at sustainable and inclusive growth.

The high-penetration integration of renewable energy requires huge demand for reliable capacity resources, and the coal-fired units are the main providers of the reliable capacity in China. This study proposes a future-oriented approach to facilitate coal-fired power’ transition through capacity market development. Focusing on China’s power market reform context, we propose a two-stage capacity market mechanism integrating annual capacity auctions and monthly capacity bidding, and design the procedural and transactional framework for coal-fired power participation. We further outline three market strategies including energy market trading, centralized capacity market trading, and renewable energy alliance leasing. Environmental costs are incorporated to construct revenue models and derive boundary conditions for coal-fired units’ decision-making. Research results reveal that current capacity prices fail to cover costs, requiring substantial market-driven price increases to achieve profitability. While stable capacity revenue can reduce medium-to-long-term and spot market prices, fostering competition between coal-fired power and renewable energy resources. However, coal-fired power remains highly sensitive to price volatility, demanding robust resilience to fluctuations. Carbon prices significantly influence capacity prices, yet excessive free carbon quota allocations weaken carbon price transmission effects, necessitating optimized quota ratios to enhance market responsiveness. Finally, policy implications are proposed according to the research results.

We examine the effects of issuing green bond on green premium and green signal transmission by matching green bonds with ordinary bonds. We find that the credit spread of green bonds is significantly lower than that of ordinary bonds, especially for those green bonds with lower information disclosure complexity. Besides, issuing green bonds cannot receive a positive response from the stock market, but can significantly reduce issuer’s loan costs and provide more financial subsidies for high polluting issuers. Furthermore, by obtaining discounted loans and financial subsidies, issuing green bonds can increase issuer’s R&D intensity and reduce their carbon emissions. These findings indicate that issuing green bonds can reduce financing costs and convey green signals to market stakeholders with less investment experience.

Based on 5-minute high-frequency trading data, we examine the time-varying causal relationship between herding behavior and multiscale spillovers (return, volatility, skewness, and kurtosis) in the Chinese stock market. We employ the novel time-varying Granger causality test proposed by Shi et al. (2018), which is based on the recursive evolving algorithm developed by Phillips et al. (2015a, 2015b), to identify real-time causal relationships and capture possible changes in the causal direction. Our findings reveal a strong relationship between herding and spillover effects, particularly with odd-moment (return and skewness) spillovers. For most of the study period, a bidirectional causal relationship was found between herding and odd-moment spillovers. These results imply that herding behavior is a key driver of spillover effects, especially return and skewness spillovers, which are primarily transmitted through the information channel. By contrast, volatility and kurtosis spillovers are more strongly driven by real and financial linkages. Furthermore, spillover effects also affect herding behavior, highlighting the intricate feedback loop between investor behavior and risk transmission.

Utilizing a unique data set of corporate site visits to Chinese capital market from 2013 to 2022, this study provides new evidence on the economic benefits brought by corporate site visits from a social network perspective. Specifically, we examine that whether information transmission through network of corporate site visits. Our results show that network centrality is positively associated with market information efficiency. This positive effect is robust and remains valid after a battery of robustness checks and endogeneity analyses, which verify the existence of information interaction in the network of corporate site visits. Furthermore, we find evidence that network of company visits positively influence market information efficiency through lowering information asymmetry between investors and listed firms rather than the “irrational factor” mechanism. In brief, our paper contributes to the existing research by presenting evidence that corporate site visits are significant venues for investors to gain and exchange information about listed companies.

With fintech growth and smartphone adoption, e-wallets, which enable instant transactions while offering cash management products with financial returns, have become increasingly prevalent. Using a unique dataset from Alipay, the world’s largest e-wallet provider, we find that holdings in Yu’EBao—an investment product usable for payments—are less affected by interest rate changes than similar assets without payment functions. This effect is stronger for users who depend on Yu’EBao for daily spending, during peak payment periods, or among less experienced investors. Our findings show that Yu’EBao reduces retail fund flow to riskier assets by 7.7% for every one-percentage-point interest rate cut, dampening monetary policy transmission through the portfolio rebalancing channel.

Using unique account-level data from a leading Chinese fintech platform, we investigate how financial influencers, the key information intermediaries in social finance, attract followers through a process of social transmission bias. We document a robust performance-following pattern wherein retail investors overextrapolate influencers’ past returns rather than rational learning in the social network from their past performance. The transmission bias is amplified by two mechanisms: (1) influencers’ active social engagement and (2) their index fund-heavy portfolios. Evidence further reveals influencers’self-enhancing reporting through selective performance disclosure. Crucially, the dynamics ultimately increase risk exposure and impair returns for follower investors.

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. This study provides some reference and guidance for cross-market investment portfolios and the regulation of China’s financial market.

Estimates of canonical affine term structure model parameters are highly sensitive to sample periods. For example, depending on whether the sample starts in 1961 or 1981, the 5-5 forward risk-neutral rate for September 1981 differs by 4.6 percentage points or 98% of the latter. The estimated response of this rate to high-frequency monetary policy shocks differs by a factor of three, even within a fixed sample for the monetary policy transmission regression. We suggest that a shifting endpoint model can mitigate these issues. Additionally, we provide new estimates of the effects of monetary policy shocks on long-term risk-neutral rates.