Proposing a comprehensive evaluation measure of the high-quality development of manufacturing industry (HQDMI), the purpose of this paper is to explore whether the high-quality development of manufacturing industry (HQDMI) promotes the application of industrial robots (AIR). Evidence shows that, first, the index of HQDMI across regions shows an upward trend from 2008 to 2020. Furthermore, the application of industrial robots significantly increases the high-quality development of manufacturing industry. Our heterogeneity analysis further infers that the positive effect of AIR on HQDMI is more pronounced in wealthy and eastern regions. The mechanism analysis shows that the application of industrial robots can generate the “value-added effect of informatization” and the “resource allocation effect”. The findings provide important theoretical support for the use of industrial robots and the promotion of high-quality development.

This study introduces a new volatility model based on dependent functional data to investigate the intraday volatility characteristics of CSI 300 in the context of high-frequency data. The volatility curve is fitted and reconstructed using three methods: functional principal component analysis, Newey-West kernel, and truncation-free Bartlett kernel. We adopt a functional time series approach for short-term dynamic forecasting. The empirical results show that the proposed dependent functional volatility estimation model based on the long-term covariance of the truncated Bartlett kernel can accurately capture the intraday volatility trajectory and outperforms other models in terms of forecast accuracy and profitability. This study improves the volatility-related research methodology, which is conducive to discovering the price formation mechanism of the stock index futures market and improving risk management capabilities.

In recent years, more and more funds circulate internally in the financial field, which is called “financial hoarding”. After calculations, the scale of China’s financial hoarding was 242,178 billion yuan in the first quarter of 2003 and jumped to 1,801,706 billion yuan in the fourth quarter of 2016, which increased by nearly 7.4 times in the past 14 years and accelerated after 2014. The phenomenon that large amounts of money deviate from the real economy to virtual economy is called “shift from real economy to virtual economy”. The large scale of financial hoarding will inevitably influence the economic growth in China. Does financial hoarding promote or inhibit the economy? Does the relationship change with the economic growth rate? To address this issue, this paper first provided theoretical analysis of the relationship between financial hoarding and economic growth. Then, it used the data of the first quarter of 2003 through the fourth quarter of 2016 in China for empirical analysis. The results revealed two facts. Firstly, the simultaneous equations model showed that financial hoarding and economic growth promote each other in the long run and financial hoarding can be conducive to economic growth. Secondly, the MS-VAR model showed that the relationship between financial hoarding and economic growth changed with the economic growth rate. In addition, financial hoarding had a positive effect on the economic growth under both medium and high economic growth regimes, but to a greater extent in high economic growth regimes.