دانلود رایگان مقاله انگلیسی شبکه ریسک سیستماتیک موسسات مالی چینی - الزویر 2018

Abstract

The Chinese stock market crash in June 2015 has demonstrated necessary to improve understanding of systemic risk from the perspective of financial network. This study constructs a tail risk network to present overall systemic risk of Chinese financial institutions, given the macroeconomic and market externalities. Employing the Least Absolute Shrinkage and Selection Operator (LASSO) method of high-dimensional models, our results show that firm’s idiosyncratic risk can be affected by its connectedness with other institutions. The risk spillover effect from other companies is the main driving factor of firm-specific risk, comparing with macroeconomic state, firm characteristics and historical price movement. The number of connections between institutions significantly increases during June 2014 to June 2016. Moreover, we utilize the Kolmogorov-Smirnov statistic to test significance of systemic risk beta based on tail risk and further rank the systemic risk contribution. Regulators could detect those firms that are most threatening to the stability of system.

Conclusion

China has attempted to or has been an indispensable part of the world economy with many achievements in its monetary and financial system, especially with respect to the progress of reforming its stock markets. Due to a series of liberalization policies, the cooperation between China’s financial institutions has made tremendous unprecedented progress that has provided opportunities for risk contagion. The Chinese stock market crash in 2015 also demonstrates the need for an improved understanding of systemic risk. Therefore, it is necessary to quantify the systemic risk of the Chinese stock market in this new situation. With the CoVaR type measure by Adrian and Brunnermeier (2016), we investigate the systemic risk of Chinese stock markets after 2010. Following the framework of Hautsch et al. (2015), we construct a tail risk network of fifty financial institutions to explore firm-specific risk, given the macroeconomic and market externalities. Employing the LASSO method of high-dimensional models, our results show that a firm’s idiosyncratic risk can be affected by its connectedness with other institutions. The risk of spillover effect from other companies is the main driver of firm-specific risk, compared with macroeconomic state, firm characteristics, and lagged return. Our results classify the firms into three categories of risk producers, risk transmitters, and risk takers within the network.