Abstract
We utilized a high dimensional financial network to investigate the systemic risk contagion between different industries in China and to explore the impacts of monetary policy and industry heterogeneity factors. The empirical results suggest that the total level of systemic risk increased quite significantly during the 2008 global crisis and the 2015–2016 Stock Market Disaster. The energy, material, industrial, and financial sectors are the top systemic risk contributors. Industry heterogeneity variables such as the leverage ratio, book-to-market ratio, return on assets (ROA) and size have significant impacts on the systemic risk, but their effects on the systemic risk contribution are more pronounced than those on the systemic risk sensitivity. Moreover, monetary policy can effectively suppress the systemic risk diffusion derived from the leverage ratio. These results are essential for investors and regulators of risk management.
Highlights
Since the 2008 global financial crisis, systemic risk contagion and its influencing factors has become the focus of academic circles and regulatory authorities
Healthcare, IT, telecommunications, and utility industries are more susceptible to systemic risk due to their high levels of risk in-degree
This paper considers price-based instruments, which mainly refers to the interest rate and quantitative monetary policy instruments, a tool that primarily applies to reserve ratios., and The interest rate indicator (RATE) is expressed in the one-year deposit benchmark interest rate, and the reserve ratio indicator (RR) is the statutory reserve ratio based on the weighted average of large financial institutions
Summary
Since the 2008 global financial crisis, systemic risk contagion and its influencing factors has become the focus of academic circles and regulatory authorities. Some non-financial industries may even play central roles in economy networks due to their special financing relationship and the socioeconomic system [8] It is of great importance for investors and regulators to be able to understand systemic risk contagion, as well as to identify its influencing factors from a global industry perspective. Barigozzi and Hallin [28] proposed LVDN methods based on the generalized dynamic factor model (GDFM) for the analysis of volatility interconnections in high dimensional series Their method has two main advantages: (i) it is based on the GDFM, which is entirely non-parametric and model-free, it can overcome curse-of-dimensionality problems in large sample estimation, and (ii) given the economic interpretation of the network indicators, it has proven to be a powerful tool for analyzing systemic risk contagion.
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