Based on the different premium volatility characteristics of various systematic factors in the A-share market, this paper constructs six representative high-frequency volatility prediction models that consider multipl...Based on the different premium volatility characteristics of various systematic factors in the A-share market, this paper constructs six representative high-frequency volatility prediction models that consider multiple complex risk structures. On this basis, a detailed comparative analysis of the differences in volatility characteristics among various factors is conducted, and the optimal prediction and early warning framework for the A-share market is proposed. Research shows that: 1) The volatility research results only for individual market indexes are not universally representative. 2) The fluctuation characteristics among different systematic factors and their respective optimal prediction model frameworks generally have significant differences, that is, there is no single fixed combination of model parameters. 3) Complex risk characteristics such as long memory, measurement errors, and high-frequency jump fluctuations obviously exist in the A-share market. The optimal forecast and early warning framework for the A-share market can be constructed by a combination of models that consider one or more of the above risk characteristics. The above conclusions have important practical reference value for the risk warning and prevention of the A-share market and the formulation of related policies.展开更多
随着工业中的线束复杂性的提高,对线束质量的检测也日渐重要,以往的检测方式已不能满足要求。文中设计了一种独立式线束自动检测系统,采用了 USB 模块、Flash 存储器等结构,达到了系统独立运行的要求。在硬件设计中采用了处理速度快功...随着工业中的线束复杂性的提高,对线束质量的检测也日渐重要,以往的检测方式已不能满足要求。文中设计了一种独立式线束自动检测系统,采用了 USB 模块、Flash 存储器等结构,达到了系统独立运行的要求。在硬件设计中采用了处理速度快功能强大的AVR-ATmega64单片机和 AT45DB161D大容量 flash 存储器;在软件设计中提出了链式存储结构和取样学习功能。该线束自动检测系统投入使用半年以上,效果良好。展开更多
文摘Based on the different premium volatility characteristics of various systematic factors in the A-share market, this paper constructs six representative high-frequency volatility prediction models that consider multiple complex risk structures. On this basis, a detailed comparative analysis of the differences in volatility characteristics among various factors is conducted, and the optimal prediction and early warning framework for the A-share market is proposed. Research shows that: 1) The volatility research results only for individual market indexes are not universally representative. 2) The fluctuation characteristics among different systematic factors and their respective optimal prediction model frameworks generally have significant differences, that is, there is no single fixed combination of model parameters. 3) Complex risk characteristics such as long memory, measurement errors, and high-frequency jump fluctuations obviously exist in the A-share market. The optimal forecast and early warning framework for the A-share market can be constructed by a combination of models that consider one or more of the above risk characteristics. The above conclusions have important practical reference value for the risk warning and prevention of the A-share market and the formulation of related policies.
