Abstract
This paper uses the 5-five-minute high-frequency data of energy-listed companies in China's A-share market to extract the jump of energy stock prices and build a dynamic stock price jump complex network. Then, we analyze the clustering effect of the complex network. The research shows that the energy stock price jump is an important part of stock price volatility, and the complex network of energy stock jump risk has obvious time-varying characteristics. However, the infection problem of stock price jump risks needs specific analysis. China's coal industry has an important influence on the development of China's energy industry. According to the clustering analysis results of the network community, the clustering effect of the network community has time-varying characteristics. After October 2017, the clustering effect of the jumping risk of the coal industry and the new energy industry is obvious. The risk contagion within the new energy industry community is a key point for the development of the new energy industry.
Highlights
With the acceleration of global economic integration, the energy finance market developed based on the energy industry and relying on the financial market has become an important global financial trading platform. e effective combination of the energy and financial markets has become the key to whether the energy market can meet the growing energy demand in the human economy
With the rapid development of energy finance, energy and financial risk management has become the key to the development of the international energy and international financial markets. e energy finance market efficiently allocates resources while spreading the risks of the energy finance market throughout the industry. is paper takes the energy stock price in China’s stock market as the research object and uses the realized jump method to measure the jump risk in the energy financial market
We use the realized jump method to obtain the jump risk of the energy stock. ird, we build complex networks for all stock price jumps to study the dynamic changes of complex networks
Summary
With the acceleration of global economic integration, the energy finance market developed based on the energy industry and relying on the financial market has become an important global financial trading platform. e effective combination of the energy and financial markets has become the key to whether the energy market can meet the growing energy demand in the human economy. Is paper takes the energy stock price in China’s stock market as the research object and uses the realized jump method to measure the jump risk in the energy financial market. To study the dynamic characteristics of a complex network of energy prices, we use Prim’s algorithm to build a complex network of energy price jumping risks. The community method is used to study the dynamic changes in the energy stock jump communities. (2) e complex network of the energy company stock price jump has obvious time-varying characteristics. (1) is paper takes the jump of the energy industry stock price as the object and studies the jumping risk problem of the energy industry by using the complex network method. (2) is paper analyzes the dynamic characteristics of the complex network of energy stock jump risk. E main contributions of this paper are as follows. (1) is paper takes the jump of the energy industry stock price as the object and studies the jumping risk problem of the energy industry by using the complex network method. (2) is paper analyzes the dynamic characteristics of the complex network of energy stock jump risk. (3) e coal industry occupies a central position in China’s energy complex network, and the risk of jumping within the coal industry community and the new energy industry community is worthy of attention
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