Researchers have recently studied the interactions between corporate and government bond issuances in a variety of countries. Some conclude that government bonds compete with private bond issuances, while others conclude the opposite. We study here the special case of China’s 2017 issuance of two sovereign bonds denominated in U.S. dollars. We find that corporate bonds experienced a decline in yield spreads, bid-ask spreads, and price volatility around the time this sovereign issuance was first announced. The results are particularly strong for corporate bonds with maturities similar to those of the USD sovereigns. We conclude that these new bonds served as useful reference instruments that helped investors price and hedge the risks impounded in Chinese corporate bonds.

This paper examines the efficiency of prediction markets by studying the markets for catastrophe (CAT) bonds, compared to previous studies of prediction markets that used small-scale observational field data or experiments. We collect actual catastrophe loss data, match the defined trigger events of each CAT bond contract, and then employ an empirical pricing framework to obtain the excess CAT premiums in order to represent the market-based forecasts. Our results indeed show that the market-based forecasts have more significantly predictive content for future CAT losses than professional forecasts that use natural catastrophe risk models. Although the predictive information for CAT events is specialized and complex, our evidence supports that CAT bond markets are successful prediction markets that efficiently aggregate information about future CAT losses. Our results also highlight that actual CAT losses in future periods can explain the excess CAT bond spreads in the primary market and provide evidence of market efficiency when pricing CAT risk.

This paper documents a stock-dividend premium of around 10% when controlling for optimistic earnings growth and liquidity improvement. We propose an alternative explanation for the effect of stock dividends from the perspective of investor recognition. First, we find that stock-dividend premiums are positively related to an increase in investor base, particularly for firms with a small investor base. Second, an increase in investor base is due to individual investors, as they, especially those with a stronger propensity to gamble, are net buyers around the announcement of stock dividends, while institutional investors behave in the opposite manner. Finally, we show that after paying stock dividends, firms experience significant increases in speculative features, which are caused by clientele shifts toward individual investors.. As a whole, our results also indicate that an increase in investor base could be related to investors' gambling preferences.

This study examines the price discovery patterns in the three BRICS countries’ stock index futures markets that were launched after 2000 – China, India, and Russia. We detect two structural breaks in these three futures price series and their underlying spot price series, and use them to form subsamples. Employing a Vector Error Correction Model (VECM) and the Hasbrouck (1995) test, we find the price discovery function of stock index futures markets generally improves over time in China and India, but declines in Russia. A closer examination not only confirms the findings of Yang et al. (2012) and Hou and Li (2013) regarding price discovery in China’s stock index markets, but also reveals the inconsistency of futures’ leading role in the price discovery process. Further, we find some evidence of day-of-the-week effects in earlier part of the sample in China, but not in India or Russia. And our GARCH model results show bidirectional volatility spillover between futures and spot in China and India, but only unidirectional in Russia.

Although China has the largest trading volume of commodity futures, limited studies have been devoted to the term structure of Chinese commodity futures. This paper takes the tools in functional data analysis to understand the term structure of commodity futures and forecast its dynamic changes at both short and long horizons. Functional ANOVA has been applied to examine the calendar e_ect of term structure in level and _nd the seasonality in the commodity futures of coking coal and polypropylene. We use an h-step functional autoregressive (1) model to forecast the dynamic change of term structure. Comparing with native predictor, in-sample and out-of-sample forecasting performance indicate that additional forecasting power is gained by using the functional autoregressive structure. Although the dynamic change at short horizons is not predictable, the forecasts appear much accurate at long horizons due to the stronger temporal dependence. The predictive factor method has a better in-sample _tting, but it cannot outperform the estimated kernel method for out-of-sample testing, except for 1-quarter-ahead forecasting.

By testing properties implied by one-dimensional diffusion option pricing models, we find that call (put) prices in the Chinese 50ETF option market move in opposite (same) direction with the underlying between 13.39% and 27.89% (between 12.45% and 33.98%) of the time for 5-minute and 1-day sampling intervals respectively. Given fundamental different investor structures in U.S. and China option markets, we also observe some important unique features in the 50ETF option price dynamics. More importantly, we demonstrate that these striking violations reduce substantially in 2016 compared with those in 2015, indicating that Chinese stock option market becomes more efficient.

本文发现，股票回报与公司的气候变化暴露水平之间存在可预测的关系。投资者并没有充分利用公司的气候变化暴露信息；那些暴露水平更高的公司，未来的回报更低。基于此构建的多空组合，能产生0.5%的因子调整后回报。本文排除了对冲气候变化风险、碳风险定价以及对ESG风险的担心等一系列替代解释。本文发现，公众的气候变化意识在近期的提高，削弱了这种可预测关系的幅度。

数字支付技术能像现金和国债那样，产生流动性溢价吗？对此，我们利用全球最大的数字支付平台（支付宝）进行估算。我们利用了以下事实：不同的货币市场基金进入该平台（因此可被用户持有和交易）的时间有先后。我们发现，一旦基金能被用于交易，它的规模平均增长了42倍。在此基础上，利用一个把基金视为不完全替代品的模型，数字支付技术的流动性溢价为年均1.0%-1.7%。

We document that firms' strategic reactions could hamper the effectiveness of RegTech. Our paper focuses on one RegTech innovation in China—online platforms launched by stock exchanges for investors to communicate with listed companies. We find that firms provide a number of no-answer replies—answers without clear, specific, or useful information—to investors' questions. Consistent with strategic information hoarding, no-answers are shown to be associated with subsequent unfavorable news. Moreover, firms with higher no-answer rates tend to have lower price efficiency, higher return volatility, and higher stock price crash risk. The evidence suggests regulators' objective of enhancing investor protection is not fully fulfilled.

More than one in five listed firms in China initiate dividend payments during the year right before their initial public offerings (IPOs). This tendency, which seems to contradict the purpose of raising capital, constitutes the pre-IPO dividend puzzle. This paper examines this puzzle using manually collected Chinese data from 2006 to 2019. We find that firms initiating pre-IPO dividends tend to have lower IPO underpricing than non-initiating firms. We also find that the effect of pre-IPO dividend initiation on IPO underpricing is more pronounced for firms with stronger pre-IPO growth and profitability. Additional analyses indicate that initiating firms have better pre- and post-IPO operating performance and post-IPO stock performance. Moreover, initiating firms pay more dividends and have significantly higher investor attention after the IPOs. Collectively, the pre-IPO dividend initiation is not a short-term strategic behavior of low-quality firms but is intended to send positive signals and improve investors’ stock valuation.