Since the 2008 global financial crisis, systemic risk contagion and its influencing factors has become the focus of academic circles and regulatory authorities. Incentives for systemic risk may come from within the financial system (such as bank failure and the collapse of financial market prices) and from external financial systems (such as macroeconomic reform and the decline of a national economy’s pillar industries) [1
]. Numerous studies have shown that non-financial industries are also associated with systemic risk [4
]. In emerging markets, 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.
Despite the global financial crisis causing a worldwide recession in recent years, China’s economy has continued to grow rapidly and it is critical to global economic growth. For example, in the first half of 2019, China’s gross domestic product (GDP) growth rate was 6.3%, which ranked it first in the world. However, during the post-financial crisis, several serious market crashes occurred in the Chinese stock market, which damaged investor confidence and the entire economy. Impressively, China’s Shanghai Composite Index fell by more than 30% over 15 days in June 2015, and then fell by nearly 45% over the next two months. The index reached a peak of 5178 points on 15 June and plummeted to 2850 on 26 August 2015. The rapid growth of China’s economy and the dramatic fluctuations in the stock market during the post-crisis period have influenced the world economic and financial markets; this has attracted significant attention from international investors. Therefore, understanding China’s systemic risk during the post-crisis period is crucial for the world market. In general, the literature justifies systemic risk contagion during financial crisis, but little is known about it in emerging market economies during the post-crisis period. In this paper, we use the 2008–2016 Chinese stock market as an example to study dynamic systemic risk contagion.
The recent literature focuses on systemic risk contagion among financial institutions (see a review by Benoit et al. [9
]), while the risk from sectors other than the financial sector are rarely mentioned. Bisias et al. [10
] assumed that systemic risk arises endogenously within the financial system and provide a broad overview of quantitative measures of systemic risk. Glasserman and Young [11
] proposed a theoretical framework for understanding the relationship between interconnections among financial institutions and financial stability. Kahou and Lehar [12
] offer a literature review of macroprudential policies and address the link between the stability of the financial system and the performance of the overall economy. Since financial institutions and real enterprises are related through credit and debt, difficulties in financial institutions may lead to the collapse of the financial system or the increase of risk, and the same situation in a real sector (or enterprise) can have a similar effect. Chiu et al. [2
] provide evidence of significant volatility and tail risk spillovers from the financial sector to many real sectors in the U.S. economy. The main reason why systemic risk can be contagious across industry is that investors tend to rely not only on the market but also tend to rely on industry-specific indices as important references for evaluating and predicting portfolio performance [13
]. Thus, our goal here is to extend the study of systemic risk from the institutional level to the industry level.
The existing literature has proposed a number of methods to measure systemic risk based on publicly available market-data such as stock price. These methods can be broadly classified into three categories: financial asset correlation [14
], tail-dependence [20
], and sophisticated networks [25
]. The representative measurements of financial asset correlation include the cross-correlation coefficient [15
] and principal component analysis (PCA) [16
]. There are four prominent examples of tail-risk measures: marginal expected shortfall (MES) and the systemic expected shortfall (SES) [23
], the SRISK of Brownlees et al. [26
], and the CoVaR of Adrian and Brunnermeier [22
]. However, the above measurements may underestimate systemic risk among financial institutions since they cannot capture the risk spillovers found in financial network topologies [26
]. Network theory provides a valuable tool for the analysis of systemic risk contagion because it can abstract the complex economic system into a network with a set of nodes and edges, revealing the inter-topological structure and complexity of the system [29
]. Although the market-data may have no particular pre-specified graphical structure, we can recover the network structure as defined by the long-term variance decomposition network model (LVDN, Diebold and Yilmaz [25
]). Another reason for adopting the complex network framework to study systemic risk is that the interconnectivity among institutions or industries often has a complicated dependency structure. For example, interdependency among the financial sector and other real economy sectors may not necessarily show monotonic linearity [31
]. In addition, investigating systemic risk for a large number of samples can lead to severe statistical deficiencies in model estimation, including overfitting, inaccurate parameter estimates, and uninformed inferences (“dimensional disasters”). 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, thus 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.
Monetary policy plays an indispensable role in the stability of financial markets, but how monetary policy implementation affects systemic risk contagion at the industry level is inconclusive. According to Reinhart and Rogoff [32
], systemic risk is closely related to monetary policy, and a tight money policy can lead to bank defaults, causing bank credit to tighten and leading to a sharp rise in systemic risk. Taylor believes that expansionary monetary policy is one of the main factors in systemic risk accumulation, which ultimately led to the global financial crisis [33
]. Battiston et al. [1
] found a correlation between debt defaults and systemic risk in the real economy. Chiu et al. [2
] found that industry characteristics help to explain the size of tail spillovers. To the best of our knowledge, current research on systemic risk has not considered the interaction between monetary policy and industry characteristics. Therefore, we are trying to fill this research gap. We take monetary policy and industry heterogeneity factors into consideration and explore their influence on systemic risk sensitivity and contribution in various industries.
Here, we use the LVDN tool based on GDFM [28
] to study system risk contagion in China. We used daily data from companies in the CSI 300 index from 4 January 2008 to 30 December 2016 to construct dynamic LVDNs and analyze the systemic risk sensitivity, systemic risk contribution, and the overall level of systemic risk from an industry perspective. Then, we studied the relationship between monetary policy, industry heterogeneity and systemic risk under the framework of panel regression analysis. The novelty of this paper is based on the following aspects:
Using the LVDN tool based on GDFM, we expand on the current literature on measuring the systemic risk at the institutional level to focus on the industry level. We found that several industries including the energy, materials, industrial, and financial sectors are the top contributors to systemic risk due to their high levels of risk out-degree. Consumer, healthcare, IT, telecommunications, and utility industries are more susceptible to systemic risk due to their high levels of risk in-degree. This not only enables investors to better allocate portfolios across sectors to reduce risk exposure, but also helps regulators to target the most systemically important sectors, and monitor risk in the whole market.
We found that the total connectedness of LVDNs increases significantly when the stability of the system exhibits distress. An increase in cross-industry connectedness caused the high systemic risk level during the 2008 global crisis and the 2015–2016 Stock Market Disaster in China. This suggests that regulatory commissions should focus on cross-industry connectedness and increase the coordination of their supervisory responsibilities.
This paper revealed that monetary policy not only directly affects systemic risk but also indirectly affects the effect of the industry’s leverage ratio. Industry heterogeneity variables have significant impacts on systemic risk, but their effect on the systemic risk sensitivity is more pronounced than their effect on the systemic risk contribution.
In Section 2
, we introduce the measure of systemic risk, i.e., the LVDN network based on the GDFM model and the panel regression models. We show the data and the empirical analysis in Section 3
and present our conclusions in Section 4
This paper seeks to shed new light on systemic risk contagion and its determinants from an industry perspective. We utilized the high dimensional financial network to capture the systemic risk contagion between different industries in China and to explore its relationships with monetary policy and industry heterogeneity factors. The results of the network analysis show that the total level of systemic risk increased significantly during the 2008 global crisis and the 2015–2016 Stock Market Disaster. The energy, materials, industrial, and financial industries are the top systemic risk contributors due to their high levels of risk output. Consumer, healthcare, IT, telecommunications, and utility industries are more susceptible to systemic risk due to their high levels of risk input. Combining the network theory and econometric analysis, we found that industry heterogeneity variables had significant impacts on systemic risk sensitivity and systemic risk contribution, but their effect on the systemic risk contribution was more pronounced. In particular, the effects of the leverage ratio and book-to-market ratio on the systemic risk contribution were positive, and the effects of the total return on assets and size on the systemic risk contribution were negative. However, for systemic risk sensitivity, only the effect of the leverage ratio and total return on assets were found to be robust. Moreover, monetary policy was shown to not only directly affect the systemic risk of the industry but also indirectly affect the effect of the industry’s leverage ratio, implying that China’s monetary policy can better restrain the adverse effects of leverage on market stability.
Our empirical study contributes to the literature on measuring systemic risk and has important economic implications in terms of asset pricing, risk management and policy making. For investors, the practical implications of our findings suggest that investment strategies should be adjusted accordingly to address risk contagion from the most influential industries to other industries. For regulators, we provide useful information when measuring the systemic risk and determining which industry are systemically important. In particular, we propose the following advice for risk supervision. Firstly, regulation should not only be placed on financial institutions but also on some entity industries. Secondly, great attention should be paid to connectivity among institutions or industries. The current economic and financial system demonstrates that more complex internal relevance and potential systematic risk attack is bound to influence entity industries through financial institutions, thus producing a scaling effect as well as a spillover effect. Ultimately, prudent regulation of monetary policy is necessary to prevent systematic risk in the mixed economy.
Our framework has several possible options for further study. First, the data used in our study do not include all publicly listed companies in China because we have eliminated companies that are not included in the CSI 300 and those that we only have limited data. Thus, developing new methods for analyzing systemic risk with limited data is a worthy goal. Another important extension would be the forecasting of systemic risk contagion in the networks. This effort could use the approach described by Barigozzi and Hallin [28
], which provides volatility forecasts for the various stocks based on the GDFM approach. Moreover, the various variance decompositions and impulse-response functions of the GDFM approach open the way for systemic risk forecasting.