一、主讲学生与论文题目：

1. 姚薇（2017级博士生）：On the relationship between the nominal interest rates and commodities’ prices in the context of the quantitative easing: Evidence from a wavelet coherency analysis

2. 刘俊（2015级博士生）：

1) The A-H Premia and Stock Liquidity: Evidence from Chinese Cross-Listed Stocks

2) How Is Illiquidity Priced in the Chinese Stock Market?

3) News tone, investor sentiment, and liquidity premium

二、时间：2023年7月2日（周日）下午14:00-17:00

三、地点：腾讯会议

四、点评与讨论教师：

杜涣程 中央财经大学欧洲杯网站_欧洲杯下注平台-官网推荐 助理教授

张欣然 中央财经大学欧洲杯网站_欧洲杯下注平台-官网推荐 助理教授

张妙音 中央财经大学欧洲杯网站_欧洲杯下注平台-官网推荐 助理教授

五、主持人：杜涣程 中央财经大学欧洲杯网站_欧洲杯下注平台-官网推荐 助理教授

六、论文摘要

1. On the relationship between the nominal interest rates and commodities’ prices in the context of the quantitative easing: Evidence from a wavelet coherency analysis

We investigate the time and frequency varying features of both the co-movement and lead-lag relationship between commodities’ prices and nominal interest rates in the context of quantitative easing (QE) policy. In this direction, we adopt a wavelet coherency analysis which is applied to U.S. data over the period from 2002:7 to 2018:6. Our results indicate that the large amount of funds and the active speculative activity in the commodity market give commodities the financial attribute, thus the positive correlation between the nominal interest rates and commodities’ real prices in the short and long run, which is in line with the Keynesian liquidity preference framework and the Fisher Effect respectively. Furthermore, our results suggest that commodities’ prices lead nominal interest rates through the inflation rate in the medium and long run before and during the QE policy period. The transmission through the inflation rate appears to be less intense in the post-QE policy period. Finally, an in-depth analysis indicate that commodities’ prices may lead interest rates including metals’ prices over the QE period, which supports the hypothesis that apart from energy and agricultural commodities’ prices, metals’ prices may also influence monetary policy through the inflation rate.

2. The A-H Premia and Stock Liquidity Evidence from Chinese Cross-Listed Stocks

As China progressess in its efforts to promote the free flow of capital, the persistent price premium of A-shares over their H-share counterparts remains a puzzle. This study utilizes a fixed effect model for initial analysis and a two-step GMM dynamic estimation method to alleviate the concern of possible endogeneity. We find a 5.62% increase in A-H premia with a standard deviation decrease in relative illiquidity, measured as Amihud (2002) ratio of A-share over the H-share. Our findings demonstrate a strong level of consistency across different exchanges, time periods, and liquidity measures. Moreover, the heterogeneous findings reveal that this effect is more prominent for state-owned firms, as well as those that have low ownership concentration, earnings quality, and institutional holdings. Furthermore, we employ a difference-in-difference approach to assess the effects of the Stock Connect program, which shows an inverse spillover effect of liquidity, leading to a surge in capital outflows in the H-share market. From a policy-making perspective, it is important to continue reforming the market liberalization, which can enhance the pricing efficiency of A-shares and bolster global investor confidence.

3. How Is Illiquidity Priced in the Chinese Stock Market?

This study investigates the liquidity premium in the Chinese stock market. We found that the expected stock returns increase monotonically with the quintile sort on characteristic liquidity with descending patterns. The characteristic liquidity premium ranges from 0.82% to 1.28% per month, which is much higher than that of their U.S. counterparts. Moreover, our multivariate decomposition approach highlights that characteristic illiquidity premiums can be explained mainly by size, idiosyncratic volatility, and momentum. The net systematic liquidity premium reaches 0.84% per month, driven mainly by commonality beta. The finding shows that the liquidity-based strategy forecasts cross-section and time-series expected returns.

4. News tone, investor sentiment, and liquid