It has been confirmed that sustainable investments contributing to environmental protection can benefit from the deterioration of air pollution, but this influence mechanism has not been fully discussed. This paper proposes a mediation model to study air pollution's influence on green bonds. Theoretically, air pollution raises public environmental awareness and perceptions of physical health risks, leading to increased public concern. Enhanced public concern drives investors' green preference and environmental responsibility, thus expanding green bond demand. Our studies show air pollution is significantly positive related to public concern. Public concern positively links with green bond investment willingness, resulting in increased volatility. The total positive effect of air pollution on green bond is partly absorbed by the effect of public concern. These findings confirm the mediation role of public concern. In addition, major crisis events (e.g., COVID-19) may hinder the mediation process by generating a negative trend and distracting the public.

Low demand for crop insurance, even when subsidized at highly favorable rates, remains a challenge for policymakers in developing countries attempting to create insurance-based farm safety nets. Evidence from a series of surveys and experiments with 477 vegetable farmers in China reveals several anomalies in farmers’ demand for crop insurance as well as deviations from predictions under both expected utility theory and cumulative prospect theory. Farmers are willing to pay higher price for neutral-frame risk protection tool than for an insurance-frame equivalent. Moreover, they tend to be more likely to purchase low-coverage than high-coverage insurance, even when high coverage provides greater subsidized value. Among risk- and loss-averse farmers, who theoretically should be more interested in adopting risk protection tools, we find less willingness to purchase high-coverage level insurance.

The crypto and noncrypto markets used to be separated from each other. We argue that with the rapid development of stablecoins since 2018, risks are now transmitted between the crypto and noncrypto markets through stablecoins, which are both pegged to noncrypto assets and play a central role in crypto trading. Applying copula-based CoVaR approaches, we find significant risk spillovers between stablecoins and cryptocurrencies as well as between stablecoins and noncrypto markets, which could help explain the tail dependency between the crypto and noncrypto markets from 2019 to 2021. We also document that the risk spillovers through stablecoins are asymmetric—stronger in the direction from the US dollar to the crypto market than vice versa—which suggests the crypto market is re-dollarizing. Further analyses consider alternative explanations, such as the COVID-19 pandemic and institutional crypto holdings, and determine that the primary channels of risk transmission are stablecoins' US dollar peg to the noncrypto market and their transaction-medium function in the crypto ecosystem. Our results have important implications for financial stability and shed light on the future of stablecoin regulation.

Over the past decade, the rapidly digitizing economy in China has attracted much attention in both academic and policy circles. Most existing studies focus on the positive impact digitalization has had on China's inclusive growth. Few of them have attempted to measure the widening digital divide and its potential impact. Using the 2017 and 2019 China Household Finance Survey (CHFS) data, this paper: (i) provides the first evidence that the digital divide has a significant negative impact on household consumption. For every unit increase in the digital divide, the level of household consumption will drop by about 28 percent; (ii) finds the negative impact stems from an integrated channel of rising unemployment, intensified liquidity constraints, and declining financial literacy; and (iii) further discloses that the digital divide has differential impacts on household consumption by category, while hinders consumption diversification. The results are robust to correcting for potential endogeneity due to sample selection, household heterogeneity, and reverse causality. Our findings shed new light on some little-documented evidence and have profound implications for related socio-economic policies that fully utilize technology to drive efficiency and inclusivity in the digital economy.

We examine the role of borrowers' digital footprints in debt collection. Using a large sample of personal loans from a fintech lender in China, we find that the information acquired by the lender through borrowers' digital footprints can increase the repayment likelihood on delinquent loans by 18.5%. The effect can be explained by two channels: bonding borrowers' obligations with their social networks and locating borrowers' physical locations. Moreover, the lender is more likely to approve loan applications from borrowers with digital footprints, even though these borrowers may occasionally have a higher likelihood of delinquency. The use of digital footprints can remain legitimate under stringent privacy protection regulations and fair debt collection practices. Our findings suggest that digital footprints, as a new type of collateral, can ultimately enhance financial inclusion by facilitating the lender's collection of delinquent loans.

We examine the information content of changes in shareholdings after private issuance of public equity (PIPE) by mutual funds that participate in PIPEs in China. The results show that the changes in shareholdings is positively related to alpha and cumulative abnormal return (CAR) for PIPE issuers with high information asymmetry, suggesting that the participating mutual funds have superior information. These results are robust after controlling for investment skill, geographic location, and alumni relation. The positive relation between shareholding change and information content is driven by PIPE issuers with weaker corporate governance. In addition, the positive relation is stronger when the placement discount is lower. These results are consistent with a hypothesis that controlling shareholders/management in Chinese PIPE firms may collude with mutual funds to do tunneling.

Although implicit guarantees are widely used in the shadow banking system, we know very little about its qualitative and quantitative properties. In this paper, we use a micro-level data set on China's shadow bank products to quantify the risk of implicit guarantees. We find a robust empirical fact that banks extend more implicit guarantees to their shadow bank debt (i.e., wealth management products) when their own default risks increase. Our result shows that this effect is particularly stronger when riskier banks plan to issue certificates of deposits in the interbank market. A simple model that is based on a signaling game is proposed to rationalize this fact. The key mechanism of the model is that as a bank's reputation becomes worse, it has stronger incentives to send positive signals to the market, i.e., to boost the realized returns of its shadow bank obligations, although it has no obligation to do so. Our findings show that implicit guarantees have nonlinear negative effects on bank fundamentals and the risk-weight of off-balance-sheet exposure should be increasing in banks' default risks.

Shadow banking in China constitutes a dual-track interest rate reform that adds a new market track beside the controlled formal banking track. Shadow banking leads to Kaldor-Hicks improvement if the gains from financing the underfunded private enterprise (PE) and reducing bank capital idleness caused by ultrahigh reserve requirements outweigh the losses from shadow banking risk. Pareto improvement is feasible as the state-owned enterprise (SOE), a potential reform loser, participates in shadow banking to transfer credit to the more productive PE. Full interest rate liberalization, which removes formal banking controls after the dual-track reform, does not warrant additional profit gain if bank credit misallocation favoring the SOE and SOE's low productivity persist.

Basket options have long been an important structured product. Although basket options have been extensively studied in the literature, there are few published papers that deal with the pricing of basket options with stochastic interest rates. This study presents two novel basket option pricing models that permit the interest rates to be random. The paper presents a powerful calculation technique for the problem when underlying stock returns are continuous. Finally, we use a regular grid method to the calculation of the formula of two-asset basket option when underlying stock returns are continuous and a mixture of both the regular grid method and a Monte Carlo method to the one when underlying stock returns are discontinuous, and sensitivity analyses are presented.

This paper uses high frequency option data to investigate the information content of option trading of AH cross listed stocks (A-shares traded in mainland China and H-shares traded in Hong Kong) and the role of the Shanghai-Hong Kong Connect in this issue. Measuring the informed trading with order imbalance, we find that the order imbalance of stock options traded in Hong Kong contains incremental information that predicts the return of corresponding A-shares traded in Shanghai after controlling for the cross-market return and volume factors proposed by Gagnon and Karolyi (2009). More important, this predictive power strengthens after the Shanghai-Hong Kong Connect, which is also supported by the evidence of comparison between the two stock crashes exactly before and after the connection. During the 2015 stock crash, the spillover effect of the two markets is significantly stronger than that during the 2008 financial crisis.