期刊：
International Journal of Finance & Economics,2023年28(2):1201-1213 ISSN：1076-9307
通讯作者：
Huang, Chuangxia
作者机构：
[Yang, Xin; Chen, Shan; Huang, Chuangxia] Changsha Univ Sci & Technol, Sch Math & Stat, Hunan Prov Key Lab Math Modeling & Anal Engn, Changsha 410114, Hunan, Peoples R China.;[Liu, Hong] Cent South Univ Forestry & Technol, Sch Econ, Changsha, Hunan, Peoples R China.;[Yang, Xiaoguang] Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China.
通讯机构：
[Huang, Chuangxia] C;Changsha Univ Sci & Technol, Sch Math & Stat, Hunan Prov Key Lab Math Modeling & Anal Engn, Changsha 410114, Hunan, Peoples R China.
关键词：
Financial institution network;jump volatility;panel data regression model
摘要：
The identification of systemically important financial institutions (SIFIs) is an important measure to deal with systemic risks. To achieve this goal, we first use generalized variance decomposition method and granger causality test to construct jump volatility spillover networks of Chinese financial institutions based on the 5-min high-frequency data. Then, out-strength and in-strength are adopted to analyze the SIFI. Finally, we use panel data regression model to investigate the determinant of the SIFIs. The empirical results show that: (a) The network density reaches a peak when the financial system under pressure during the China's stock market disaster of 2015. (b) Large banks and insurances usually display systemic importance, while some small financial institutions are also SIFIs due to their high value of out-strength and in-strength. (c) There are obvious differences in the factors that affect the out-strength and in-strength based on panel data regression model, but turnover rate, jump volatility, firm size and growth rate of total assets are the common driving factors.
摘要：
With the increase in energy consumption in China, the influential factors and contributions to such increase have received more attention. In this paper, the decomposition of energy consumption and energy intensity in China from 1995 to 2015 was conducted by using a complete decomposition model. This study focuses on regional difference, which is not in line with the existing studies. The decomposition results indicate that the economic growth has a significant driving effect on the energy consumption, while the energy intensity effect reduces the growth of the total energy consumption. The structural effect plays a negative role but has less effect on the growth of the energy consumption. The contribution rate on the growth of the energy consumption in the eastern region gradually decreases. However, in the western region it grows fast and even exceeds that of the eastern region to become the highest energy consumption area in China in 2010–2015. The energy intensity declines substantially in China. It is mainly due to the improvement of regional energy efficiency and negligible contribution from the structural effect. In conclusion, the energy consumption and energy intensity in China follow different forms in different regions. Therefore, the application of differentiated regional energy policies is preferable over a single national energy policy.
