Exploring Systemic Risk Dynamics in the Chinese Stock Market: A Network Analysis with Risk Transmission Index
Abstract
:1. Introduction
 How can we quantify the interflow of systemic risk in the network of Chinese stock markets? We construct a systemic risk network for individual stocks, observe how systemic risk propagates within the stock market, explore the major risk output and input centers, and analyze the primary constituents of systemic risk in the Chinese stock market.
 How does the major systemic risk contributors influence the systemic risk across communities and over time? We employ clustering algorithm on the risk dynamics over time to obtain small time periods, and then find out how the major risk centers behave regarding both detected communities and time periods in the system risk network we construct for the Chinese stock market.
2. Materials and Methods
2.1. CoVaR Based on a Single Index Model
2.1.1. Estimation of VaR and CoVaR
2.1.2. Risk Fluid in the Network
2.2. Systemic Risk Network Analysis
2.2.1. Identifying Input and Output Risk Centers by HITS Algorithm
Algorithm 1 HITS 

2.2.2. Identify Constituents of Systemic Risk by Community Detection Algorithm
Algorithm 2 Community detection. 

2.2.3. Identify Time Pattern of Systemic Risk by DBSCAN Algorithm
Algorithm 3 DBSCAN 

3. Results
3.1. Sample and Data
3.1.1. Data Description
 The “Pareto Principle” or the “80/20 Rule” is evident in the Chinese stock market. This principle, first proposed by the renowned Italian economist Vilfredo Pareto in 1897, states that 20% of the population holds 80% of the wealth. In the context of the stock market, this translates to the fact that 20% of the stocks tend to be profitable in the long term, while the remaining 80% often incur losses (Wu et al. 2010). This suggests that using a smaller subset of stocks to represent the overall market is reasonable. By calculating the longterm returns for each stock based on the annual average closing prices in 2012 and 2022, we observe that approximately 73% of the stocks in the market have zero or negative returns, which aligns with the “80/20 Rule”.
 The industry distribution of the selected stocks is similar to that of the broader market. As depicted in Figure 3a,b, the industry distribution of the 107 stocks chosen in this study closely mirrors the industry distribution of the overall market, effectively reflecting the operational characteristics of the securities market.
 The selected stocks in our study exhibit significant market capitalization and liquidity. As shown in Figure 2a,b, using the average number of shares per company as a measure of market size, the selected stocks in this study have an average number of shares that is 3.91 times higher than the market average. Additionally, using the latest available data on the number of outstanding shares as a measure of liquidity, the average number of outstanding shares for the selected stocks in this study is 11.42 times higher than the market average.We do not choose the Shanghai 50, CSI 300, or STAR 50 for the following reasons:
 Shanghai 50: The selection criteria for Shanghai 50 are essentially the same as those for Shanghai 180, but it comprises only 50 stocks, which provides a less comprehensive representation of the stock market due to its smaller sample size.
 CSI 300: Within the CSI 300, 30% of the stocks come from the financial industry, which does not align well with the industry distribution of the overall market.
 STAR 50: The STAR 50 index consists of the 50 largest STAR Marketlisted companies, which are relatively newer and may not provide sufficient data for analysis due to their recent establishment.
3.1.2. Macroeconomic Indicators
 Foreign Trade Indicators reflect the foreign trade status of a country or region, the level of foreign trade activities, and international competitiveness.
 Real EstateRelated Indicators reflect the activities and investment conditions in the real estate market.
 ConsumerRelated Indicators reflect the consumption behavior and capacity of residents, indicating the strength of economic consumption activities and changes in consumer confidence.
 Energy LogisticsRelated Indicators reflect the activity level and demand situation in the financial market’s energy and logistics sectors.
 CommodityRelated Indicators reflect the supply and demand relationships, cost pressures, and market price fluctuations of commodities. They hold significant reference value for economic analysis and decisionmaking.
 Financial Market Indicators reflect the operational status of the financial market and interest rate levels.
 Macroeconomic Overall Indicators reflect the overall economic scale and growth conditions of a country or region, indicating the overall economic development and the relative contributions of various industries.
3.1.3. Data Preprocessing
 Unprocessed Stock Price Data. Before any transformation, the ADF tests show that only five stocks’ price series out of all the stocks are stationary. Subsequently, we take the logarithm of the price data and then differentiate it. The ADF tests are performed again, and the results indicate that, after the stationarity transformation, all stocks reject the nonstationary null hypothesis, thus rendering the price series stationary. Consequently, we utilize the rolling forecast to obtain biweekly stock price data with the rolling window being 5 years. We illustrate the transformation progress with the ACF plot of Shanghai Pudong Development Bank (C600000, Shanghai, China) before and after stationarity transformation in Figure 4.
 Macroeconomic Indicators. Among all the daily and monthly data, only the monthly CPI data pass the ADF test. By observing ACF and PACF plots and relying on empirical knowledge, we determine the appropriate transformation methods for each indicator. After applying these transformations, all data become stationary.Regarding quarterly data, due to the large time intervals and relatively limited data spanning only 10 years with 45 data points, the ADF test alone cannot effectively confirm the stationarity of the data. Additionally, there is significant volatility in the quarterly data during the period from 2020 to 2022, which we believe can influence the overall stationarity of the data. Therefore, we employ empirical rules and refer to ACF and PACF plots as well as the reduction in pvalues from the ADF tests to decide on the data transformation methods, which partially improve data stationarity. The specific methods used for stationarity transformation of the macroeconomic data are detailed in Table 1. Moreover, we illustrate such effect of 1st difference for Interbank Repo Benchmark Interest Rate in Figure 5.Finally, we utilize cubic spline interpolation to adjust all indicators to a biweekly time cycle.
 Threefactor Model and Balance Sheet. The ADF tests show that all the data are stationary at a significance level of 0.05, and no further stationarity processing is required.
3.2. Systemic Risk Network Analysis of the Chinese Stock Market
3.2.1. Feasibility Testing of QR–Lasso Model
3.2.2. Feasibility Testing of Systemic Risk Network
3.2.3. Primary Output and Input Centers of Systemic Risk
3.2.4. Industry Composition of Systemic Risk
 Communities 1, 2, and 3 exhibit a diverse composition, encompassing emerging industries such as fintech, biopharmaceuticals, optical fiber, and emerging manufacturing sectors. Community 2 comprises automotive manufacturing entities, exemplified by corporations such as SAIC Motor Group (104, Shanghai, China) and Fuyao Glass Industry Group Company Ltd (660, Fuzhou, China). Conversely, Community 3 is primarily characterized by healthcare enterprises, notably encompassing pharmaceutical companies such as Hengrui Pharmaceutical (276, Lianyungang, China) and Huahai Pharmaceutical (521, Taizhou, China).
 Community 4 is primarily led by the insurance industry, including PingAn, CPIC and NCI.
 Community 5 is primarily dominated by real estate, transportation, construction, and manufacturing sectors, with some closely associated entities in the banking industry. This sector includes enterprises such as the China Railway Construction Corporation 1186, Beijing, China), China Shipbuilding Industry Company Limited, and Industrial and Commercial Bank of China (ICBC, 1398, Beijing, China).
3.2.5. Systemic Risk over Time
3.2.6. Dynamics of Systemic Risk Structures in Chinese Stock Market
4. Discussion
4.1. Conclusions
 By analyzing the four conventional network metrics—indegree, outdegree, closeness centrality, and betweenness centrality—insurance companies serve both as the main contributors and major receivers of the systemic risk in the Chinese stock. No single stock significantly influences the others, but companies like Hang Seng Electronics play a pivotal role in risk propagation.
 By analyzing the main risk output and input centers obtained from the HITS algorithm, we find that the biggest risk output centers are PingAn, CPIC and NCI; the biggest risk input centers are CLIC and NCI.
 By examining the temporal evolution of systemic risk in the Chinese stock market, we conclude that a pre2020 period is characterized by relatively low systemic risk. However, the onset of the COVID19 pandemic’s initial wave instigates significant shifts. Stringent pandemic control measures precipitate disruptions in the supply chain, production halts in select industries, and a pervasive sense of market tension, thereby engendering a noteworthy upsurge in systemic risk within the Chinese stock market. After the implementation of effective COVID19 control measures, systemic risk reverts to normal levels in early 2021.
 Moreover, an exploration of community characteristics derived from a community detection algorithm underscores that the sources of risk in the Chinese stock market from 2018 to 2022 predominantly manifest within sectors such as the secondary industry, emerging industries, and insurance. Each of these categories exhibits a pronounced internal correlation. The classification approach employed herein primarily hinges on the interplay of risk among companies, differing from conventional categorizations such as insurance, financial services, and others. Within this framework, the principal sources of risk emanate from the insurance sector. It is plausible that events such as pandemics can induce systemic risk in the insurance industry, and as these entities engage in nontraditional business activities, such endeavors may further contribute to systemic risks within the network.
4.2. Limitation and Further Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ACF  Autocorrelation Function 
ADF  Augmented Dickey–Fuller 
CLIC  China Life Insurance Company Ltd. 
CNPC  PetroChina Company Ltd. 
CPIC  China Pacific Insurance Company Ltd. 
CoES  Conditional Expected Shortfall 
CoVaR  Conditional Value at Risk 
DBSCAN  DensityBased Spatial Clustering of Applications with Noise 
ES  Expected Shortfall 
HITS  HyperlinkInduced Topic Search 
MES  Marginal Expected Shortfall 
NCI  Xinhua Insurance 
RTI  Risk Transmission Index 
PACF  Partial Autocorrelation Function 
PingAn  Ping An Insurance 
SCAD  Smoothly Clipped Absolute Deviation 
SES  Systemic Expected Shortfall 
SIFI  Systemic Important Financial Institution 
SZ180  Shanghai 180 Index 
VaR  Value at Risk 
Note
1  Ping An Bank is listed on the Shenzhen Stock Exchange, so we didn’t include it in our research. 
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Macroeconomic Indicators  Frequency  Category  Stationarity Transformation 

Export Value (Current value)  Monthly  Foreign Trade Indicators  Log 1st Difference 
Import Value (Current value)  Monthly  Log 1st Difference  
Real Estate Development Composite Prosperity Index (Current value)  Monthly  Real Estate Related Indicators  1st Difference 
Fixed Asset Investment Completed Value (Accumulated YoY)  Monthly  1st Difference  
Real Estate Development Investment Completed Value (Accumulated value)  Monthly  12th Difference  
Total Retail Sales of Consumer Goods (Current YoY)  Monthly  Consumer Related Indicators  1st Difference 
Consumer Price Index (CPI) (Current YoY)  Monthly  None  
Consumer Confidence Index (Current value)  Monthly  1st Difference  
Per Capita Disposable Income of Urban Residents (Accumulated value)  Quarterly  4th Difference  
Per Capita Consumption Expenditure of Urban Residents (Accumulated value)  Quarterly  4th Difference  
Value Added of Wholesale and Retail Trade (Current YoY)  Quarterly  4th Difference  
Electricity Generation Output (Accumulated value)  Monthly  Energy Logistics Related Indicators  12th Difference 
Total Freight Volume (Accumulated value)  Monthly  12th Difference and 1st Difference  
Railway Freight Volume (Accumulated YoY)  Monthly  1st Difference  
Purchasing Price Indices of Raw Material (PPIRM) (Current value)  Monthly  Commodity Related Indicators  1st Difference 
Retail Price Index (RPI) (Current YoY)  Monthly  1st Difference  
Producer Price Index (PPI) (Current YoY)  Monthly  1st Difference  
Corporate Goods Price Index (CGPI) (Current YoY)  Monthly  1st Difference  
China Commodity Price Index (Current value)  Monthly  1st Difference  
Money & Quasimoney(M2)  Monthly  1st Difference  
Interbank Repo Benchmark Interest Rate (Current value)  Daily  Financial Market Indicators  1st Difference 
Weighted Average Overnight Interbank Borrowing Rate (Current value)  Monthly  1st Difference  
China Government Bond Yield (10year)  Monthly  1st Difference  
Total Outstanding Loans of Financial Institutions (Domestic and Foreign Currency) (Current YoY)  Monthly  1st Difference  
Total Social Financing Scale (Stock) (Current YoY)  Monthly  1st Difference  
Keqiang Index (Accumulated value)  Monthly  Macroeconomic Overall Indicators  1st Difference 
Gross Domestic Product (GDP) (Current value)  Quarterly  Log 1st Difference  
Value Added of the Primary Industry (Current value)  Quarterly  4th Difference  
Value Added of the Secondary Industry (Current value)  Quarterly  4th Difference  
Value Added of the Tertiary Industry (Current value)  Quarterly  4th Difference 
Model  25% Quantile of Proportion Exceeding Estimated VaR  75% Quantile of Proportion Exceeding Estimated VaR  Proportion Passing Backtesting (0.05) 

QR–Lasso Model  0.88  0.91  98.1% 
Quantile Regression Model  0.78  0.82  14.0% 
Indicator  Definition  Interpretation 

Degree  ${\sum}_{j\ne i}M{C}_{i,t}\times \left{\widehat{D}}_{i\mid j}^{t}\right\times M{C}_{j,t}+{\sum}_{j\ne i}M{C}_{j,t}\times \left{\widehat{D}}_{i\mid j}^{t}\right\times M{C}_{i,t}$  Judge if the network follows a powerlaw distribution 
Indegree  ${\sum}_{j\ne i}M{C}_{i,t}\times \left{\widehat{D}}_{i\mid j}^{t}\right\times M{C}_{j,t}$  Total systemic risk transmitted by individual stocks 
Outdegree  ${\sum}_{j\ne i}M{C}_{j,t}\times \left{\widehat{D}}_{i\mid j}^{t}\right\times M{C}_{i,t}$  Total systemic risk received by individual stocks 
Closeness Centrality  ${C}_{B}\left(i\right)=1/{\sum}_{y}d(i,j),$ where $d(i,j)$ is the shortest path from i to j  Indicates whether a stock has a dominant role in other stocks’ CoVaR 
Betweenness Centrality  $g\left(v\right)={\sum}_{s\ne v\ne t}{\sigma}_{st}\left(i\right)/{\sigma}_{st},$ where ${\sigma}_{st}\left(i\right)$ calculates if i lies on the shortest path between s and v  Measures a stock’s ability to propagate risk within the system 
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Zeng, X.; Hu, Y.; Pan, C.; Hou, Y. Exploring Systemic Risk Dynamics in the Chinese Stock Market: A Network Analysis with Risk Transmission Index. Risks 2024, 12, 56. https://doi.org/10.3390/risks12030056
Zeng X, Hu Y, Pan C, Hou Y. Exploring Systemic Risk Dynamics in the Chinese Stock Market: A Network Analysis with Risk Transmission Index. Risks. 2024; 12(3):56. https://doi.org/10.3390/risks12030056
Chicago/Turabian StyleZeng, Xiaowei, Yifan Hu, Chengjun Pan, and Yanxi Hou. 2024. "Exploring Systemic Risk Dynamics in the Chinese Stock Market: A Network Analysis with Risk Transmission Index" Risks 12, no. 3: 56. https://doi.org/10.3390/risks12030056