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.

公司债能否合理定价反映了中国资本市场是否公正、有效地运行。针对中国尚不成熟的金融市场，在分析公司债信用利差时，除了关注微观因素外，更加应该重视宏观因素。本文选取的样本数据为2007年至2016年共10年的中国公司债到期收益率的周面板数据；宏观因素不仅包括如消费者物价指数、采购经理人指数、股票市场指数等传统宏观因素的指标，同时纳入了对于中国金融市场而言影响力度日益增大的固定资产投资额、货币供应量、以及工业发电量等多维度的宏观指标。利用控制时间效应的固定效应模型，实证分析了以上宏观因素对公司债信用利差的影响方向和内在传导机制。结果表明：结构化模型的解释力随公司债信用评级的降低而升高；货币供给量M1与M2的增幅与中国公司债信用利差为正相关，但回归结果不十分显著；消费者物价指数的回归系数在不同评级的债券中出现了显著正负交替的现象，这可能是受中国市场主体近年来对持续的物价上涨，存在适应性与理性这两种不同的预期所致；在与公司债信用利差正相关的宏观因素中，除了采购经理人指数、沪深300股指以及工业发电量外，固定资产投资额最为显著，表明固定资产投资对公司债定价影响最大。

政府补助是激励绿色技术投资的常用手段。我们利用中国公司的数据发现，有关政府补助能否持续的不确定性，会抑制政策的预期效果。我们的识别基于能影响官方空气质量文本的外生天气变化，因为政府补助的分配依赖于之。我们发现，在由于天气原因导致政府补助波动较大的城市里，公司对绿色技术的投资、专利申请、研发人员都更少，特别是采矿业、制造业、绿色技术产业。本文认为政策稳定性能鼓励长期绿色技术投资，实现生态文明的目标。

小微企业在我国规模小，数量巨大，作为市场经济中最活跃的主体，但小微企业一直存在融资困难的瓶颈，其生存发展问题令人堪忧。随着互联网向金融方向的发展，并在2013初步形成互联网金融的雏形，互联网与创痛金融的结合，打破了传统金融的壁垒，给小微企业融资难得问题带来希望。本文正是从互联网金融和小微企业之间的联系出发，寻求小微企业融资问题的突破口，找到二者的共同发展之路。

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.

2018年受去杠杠政策影响，大量民营企业陷入流动性危机，为此，国家号召各地政府成立纾困基金纾困上述民营企业。本文以政府对民营上市公司股权纾困的130个样本为例，基于各地政府响应国家号召对民营企业纾困这一准自然实验，用DID等方法检验了政府纾困民营上市公司的经济后果，研究发现：（1）在短期内，政府纾困起到了稳定市场的作用。具体而言，接受政府纾困的民营上市公司公告之后市场有显著的正面反应，同时被纾困方股权质押风险得到了缓解。（2）但在长期来看，政府纾困并没有改善被纾困民营上市公司的经营绩效，这体现在纾困完成后，公司的经营业绩表现更差。（3）并且，在纾困中，纾困双方信息不对称程度越高、代理问题越严重，政府的长期纾困效果越差。换言之，纾困方越有可能成为“接盘侠”。本文为政府维护金融市场稳定发展提供了实证依据，也为后续进一步规范政府纾困行为提供指导性的政策参考。

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.