Systemic Risk In China Research Articles

Managerial macroeconomic perception and systemic risk in China

This paper constructs a managerial macroeconomic perception index based on the textual financial disclosures and examines its impact on systemic risk in China. We reveal a significant positive relationship between managerial macroeconomic perception, particularly the forward-looking managerial macroeconomic perception, and bank systemic risk. Our study shows that managers with optimistic perception about macroeconomic conditions tend to engage in excessive risk-taking activities and increase the risk contagion among banks. Heterogeneous analyses suggest that the positive relationship between manager macroeconomic perception and systemic risk is more pronounced among banks that are non-state-owned, exclusively listed in A-shares, highly leveraged, or not considered systemically important. We also show that macroeconomic policy can mitigate the negative impact of managerial macroeconomic perception on systemic risk. These findings highlight the substantial role that managerial macroeconomic perception plays in shaping systemic risk within the banking sector.

Reining in the riskiest? Evidence of non-linear impacts of macroprudential regulations on bank systemic risk in China

We investigate the impact of macroprudential regulations on systemic risk in Chinese banks. Applying the approach of unconditional quantile regressions to a panel dataset of major Chinese banks from 2002 to 2018, we find supportive evidence for non-linear impacts of macroprudential regulations in mitigating the systemic risk of banks. Specifically, the risk-reducing effects of macroprudential regulations increase with the distributional quantiles of bank systemic risk, implying that macroprudential regulations may be implemented more rigorously on banks with a greater potential to influence systemic stability.

Risk Assessment of Banks and Financial Institutions under COVID-19 Crisis

Systemic risk refers to risks that have a broad and severe impact on the entire financial system or economic system. There are similarities in the definition of systemic risk in China and abroad, which usually refer to threats to the entire financial system or economic system, which can trigger a chain reaction and lead to a systemic collapse. In terms of characteristics and measurements, there may be some differences in views and methods at home and abroad. While foreign countries usually pay more attention to the interconnectedness of financial institutions, market liquidity, macroeconomic factors, etc., China may pay more attention to factors such as the stability of financial markets and the role of state-owned enterprises and governments in the economy. The shortcomings of systemic risk abroad may lie in the fact that the unique circumstances of some characteristic countries or regions are sometimes overlooked, because these factors are not universally applicable on a global scale. Due to its unique environment of large economic size, complex financial system, government intervention, and the influence of state-owned enterprises, the study of systemic risk is particularly important for China. This particular situation may require unique methods and metrics to measure and address risks to ensure the stability and sustainability of China's financial system. It is precisely in view of the lack of foreign research on China's specific context that it is of great significance to study the systemic risk of China's banking industry under China's financial system.

Research on systemic risk of China's bank-asset bipartite network

Systemic risk caused by banks due to common asset holdings serve as a significant contagion channel. In this study, we use empirical data from Chinese banks to construct a bank-asset bipartite network, employ the DebtRank algorithm for risk measurement, and incorporate asset price correlation into the DebtRank algorithm. Then we show the changes of the systemic risk in the Chinese banking system from 2018 to 2021. Furthermore, we analyze the systemic risk triggered by different types of banks and different industry assets and quantify the impact of each asset under different stress scenarios. We also conduct a validity analysis of asset price correlation, finding that the systemic risk considering asset price correlation is higher than that without considering asset price correlation. This study of financial systemic risk under the bank-asset bipartite network provides a new perspective for the regulation of systemic risk and is of significant importance for the prevention of systemic risk.

Measuring systemic risk in China: a textual analysis

PurposeThe purpose of this paper is to construct an index for systemic risk in China.Design/methodology/approachThis paper develops a systemic risk index for China (SRIC) using textual information from 26 leading newspapers in China. Our index measures the systematic risk from 21 topics relating to China’s economy and provides narratives of the sources of systemic risk.FindingsSRIC effectively predicts changes in GDP, aggregate financing to the real economy and the purchasing managers’ index. Moreover, SRIC explains several other commonly used macroeconomic indicators. Our risk measure provides a helpful monitoring tool for policymakers to manage systemic risk.Originality/valueThe paper construct an index of systemic risk based on the information extracted from newspaper articles. This approach is new to the literature.

Managerial Macroeconomic Perception and Systemic Risk in China

Managerial Macroeconomic Perception and Systemic Risk in China

Research on the tail risk contagion in the international commodity market on the China's financial market: based on a network perspective

PurposeConsidering the frequency of extreme events, enhancing the global financial system's stability has become crucial. This study aims to investigate the contagion effects of extreme risk events in the international commodity market on China's financial industry. It highlights the significance of comprehending the origins, severity and potential impacts of extreme risks within China's financial market.Design/methodology/approachThis study uses the tail-event driven network risk (TENET) model to construct a tail risk spillover network between China's financial market and the international commodity market. Combining with the characteristics of the network, this study employs an autoregressive distributed lag (ARDL) model to examine the factors influencing systemic risks in China's financial market and to explore the early identification of indicators for systemic risks in China's financial market.FindingsThe research reveals a strong tail risk contagion effect between China's financial market and the international commodity market, with a more pronounced impact from the latter to the former. Industrial raw materials, food, metals, oils, livestock and textiles notably influence China's currency market. The systemic risk in China's financial market is driven by systemic risks in the international commodity market and network centrality and can be accurately predicted with the ARDL-error correction model (ECM) model. Based on these, Chinese regulatory authorities can establish a monitoring and early warning mechanism to promptly identify contagion signs, issue timely warnings and adjust regulatory measures.Originality/valueThis study provides new insights into predicting systemic risk in China's financial market by revealing the tail risk spillover network structure between China's financial and international commodity markets.

Does financial cycle affect corporate bond ratings? – Evidence from macro and micro interaction effects

In recent years, China's bond market defaults frequently, and the credibility of credit rating agencies needs to be tested. The disorganized bond market increases the systemic risk in China, which is not conducive to the smooth operation of the social economy. This paper studies effects of financial cycle on credit rating by constructing a game model between enterprises and credit rating agencies. Regression test results of A-share listed companies from 2010 to 2019 show that the financial cycle can promote the willingness of enterprises to hold bonds, which affects the further game between enterprises and rating agencies and promotes the "pro-cyclical" upgrade of ratings. Further exploration shows that large-scale, non-listed and non-industrial companies are more sensitive to financial cycles. The contributions of this paper are as follows: First, this paper discusses the mechanism by which financial cycle affects credit rating through the influence of corporate bond financing behaviour, and expands a new research perspective for the study of how macroeconomic factors affect credit rating; Second, it clarifies the logic of credit rating changes. In the modelling process of this paper, the influence of complex endogeneity is fully taken into account, and it is verified that when the external credit supply changes, corporate financing decisions will be affected first, and credit ratings will be affected later. The research conclusions of this paper have important implications for how to regulate the behaviour of rating agencies and improve the quality of ratings.

Measurement and contagion modelling of systemic risk in China's financial sectors: Evidence for functional data analysis and complex network

We use the daily data of 45 listed financial institutions between January 1, 2008 and January 31, 2022 to conduct a functional data analysis (FDA) to measure the systemic risk of China's financial system and construct a complex network to analyze the contagion mechanism of systemic risk within and between sectors. We find that the constructed systemic risk indexes identify the influential events in the sample period and that the securities sector has a higher systemic risk than banking and insurance sectors do. The systemic risk contagion complex networks provide evidence of significant causal relationships between different sectors. Banking sector contributes a higher systemic risk and acts as a systemic risk transmitter in normal times, whereas securities and insurance sectors are more vulnerable during extreme events and may become sources of systemic risk during such events.

Research on the Spillover Effects of Systemic Risk in China's Financial Market on The Securities Industry

In recent years, with the increasing openness of China's financial market, continuous deepening of financial reforms, and growing financial innovation, the Chinese securities market has become increasingly complex, leading to a gradual increase in the probability of systemic risk. This study focuses on the daily closing prices of stocks from eight large-scale listed bond companies, including CITIC Securities and GF Securities, and incorporates the stock, currency, bond, foreign exchange, and real estate markets into the CoVaR model to analyze the spillover effects and transmission channels of these five markets on systemic risk in the securities industry. The empirical results indicate that the stock market, currency market, bond market, and foreign exchange market have significant negative spillover effects on systemic risk in the securities industry, while the real estate market has a positive spillover effect. The empirical results also suggest that among the transmission channels of spillover effects, the currency market exhibits the strongest risk volatility, individual securities companies have the highest sensitivity to changes in the stock market risk, and compared to the real estate market, the rich information content of the four financial sub-markets has a greater impact on the spillover effects of systemic risk in the securities industry.

Systemic risk spillover of financial institutions in China: A copula-DCC-GARCH approach

In this paper, we study the systemic risk spillover between several financial sectors and the stock market in China from 2009 to 2021 using the copula-DCC-GARCH model. The financial sectors concerned include banking, security, insurance, real estate, and other finance sectors. We first obtain time-varying correlations between financial sectors and the stock market from the copula-DCC-GARCH model. Furthermore, we find evidence of high dynamic correlations between the financial sectors and the stock market, varying from 0.7 to 0.9, indicating a strong risk linkage between the financial sectors and the stock market. Simultaneously, we analyze the spillovers between the financial sectors and the stock market by conditional VaR and the delta CoVaR and employ the bivariate Student-t-copula function to calculate CoVaR and ΔCoVaR. When we consider the risk spillovers from financial sectors to the stock markets, the results suggest that the highest risk spillover occurs in the banking sector, followed by the security and real estate sectors, showing that the banking sector remains a significant source of systemic risk in China. Besides, we discover that among the financial sector risk spillovers, the security industry has the highest risk spillover. As a result of the real estate sector's significant role in the financial system, it has now emerged as one of the security and banking sectors' most significant sources of risk. From the dynamic of the stock market CoVaR, it seems that the CoVaR of the stock market by a different risk source will drop rapidly (loss rise) when a major risk event occurs. Our findings provide important risk warnings and management implications for regulators, investors, and portfolio managers.

Analysis of Systemic Risk in the Chinese Insurance Market Based on Reinsurance Networks

Since the financial crisis in 2008, regulators around the world have been committed to understanding and mitigating the potential systemic risks brought by the insurance industry, focusing on the risk transmission field within the insurance industry. This paper focuses on the possible transmission relationship of systemic risk in China's insurance market through the reinsurance network from the perspective of macro-prudential, discusses the accumulation and contagion effect of systemic risk over time, and puts forward suggestions on the macro-prudential supervision of China's insurance industry.

Measurement and prediction of systemic risk in China’s banking industry

This research uses a hybrid systemic risk indicator (rSYR) to measure the systemic financial risk of China’s banking industry from 2009 to 2019 and combines rSYR with sSYR (new standardized rSYR) to more accurately determine systemic important banks. We also forecast systemic risk in the next period, finding that large-scale banks (such as ICBC, Bank of China, Agricultural Bank of China, and China Merchants Bank) have high systemic importance. After eliminating the impact of scale, we then pay attention to the possibility of systemic risk brought by some smaller banks (such as Huaxia Bank and Everbright Bank). Through the prediction of systemic risk in the next six months, we also find out that the possibility of systemic risk caused by possible capital shortage brought by Agricultural Bank of China, Ping An Bank, Bank of China and Everbright Bank is more obvious, which is worth paying greater attention.

Measuring Systemic Risk in China: A Textual Analysis

Measuring Systemic Risk in China: A Textual Analysis

Dynamic Forecasting for Systemic Risk in China’s Commercial Banking Industry Based on Sequence Decomposition and Reconstruction

Dynamic Forecasting for Systemic Risk in China’s Commercial Banking Industry Based on Sequence Decomposition and Reconstruction

Identification of systemically important banks in China based on Merton-Shapley model and a comparison between different indicators

Accurately measuring systemic risk in the banking industry and identifying systemically important banks are significant parts of macro-prudential supervision. However, there is a lack of research on measuring systemic risk and identifying important banks under the same framework. And there are relatively few studies comparing different indicators of systemically important banks in the context of China's banking industry. Therefore, this paper first constructs a new Merton model to measure the dynamically evolving systemic risk of China's banking system. Then combine the Merton model with the Shapley value to construct the Merton-Shapley framework, and propose a new indicator MShv to identify systemically important banks. At last, use the data of China's banking industry from 2006 to 2019 to conduct an empirical study. The research results show that the systemic risk in China peaked in 2008 and 2015 near the stock market crash, after 2008 the risk appeared a fluctuating decline. And it is found that MShv can accurately identify important banks and simultaneously is conducive to policy implementation. By a comparison between MShv and other systemic risk contribution indicators such as ΔCoVaR, MES, and SRISK, we find that there are many differences for different indicators in the applicability of identifying systemically important banks, the information reflected, and so on. Regulators can consider using several indicators synthetically to supervise banks during different risk periods, in order to maintain the stability of the financial system.

The Co-Movements of Credit Default Swap Spreads in China

ABSTRACT In this paper, we study systemic risk in China using information from the credit default swap (CDS) data of Chinese firms. We find a large time variation in CDS spreads. More importantly, firms’ CDS spreads co-move with each other and the first three principal components (PCs) explain 94% of the time-series variation in CDS spreads. We further identify a set of economic risk factors that drive the co-movement of CDS spreads. Large external economic shocks shift a significant proportion of the variance explanation power from the factors related to China’s domestic economic condition to foreign trade and money supply. Our results reveal the sources and dynamics of systemic risk in China.

Financial constraint, cross-sectoral spillover and systemic risk in China

This paper examines the effect of financial constraints as a transmission channel of cross-sectoral spillover of systemic risk in China using the impulse response separation technique and Diebold & Yilmaz’s (2014) spillover matrix under the STVAR modeling framework. The results show that financial constraints do have an impact on both financial and real sector risks. Financial constraints provide a transmission channel for risk spillovers among sectors, which has a greater impact on asset prices and bank loans. In addition, financial constraints also affect the overall magnitude of risk spillovers in the economy, as well as the direction of risk spillovers.

Do we need to consider multiple inter-bank linkages for systemic risk in China’s banking industry? Analysis based on the multilayer network

We construct an inter-bank multilayer network that includes direct linkages through interbank transactions and indirect linkages through bond investment, and investigate the systemic risk in Chinese commercial banks in case of credit shocks, liquidity shocks and asset price shocks. According to our study, indirect linkages contribute more to systemic risk than direct linkages, and systemic risks from different types of shocks have different characteristics. We also find an intriguing non-linear effect in the aggregation of different linkages in the multiplex structure by comparing the total risk and the sum of systemic risk of all layers. This study suggests that the regulator should pay attention to the pervasiveness and dynamics of systemic risk, with a particular focus on risk contagion caused by indirect linkages among commercial banks.

Downside and upside risk spillovers from commercial banks into China’s financial system: a new copula quantile regression-based CoVaR model

In this paper, we investigate the downside and upside risk spillovers from three kinds of commercial banks (state-owned commercial banks (SOCBs), joint-stock commercial banks (JSCBs) and city commercial banks (CCBs)) to China’s financial system by proposing a new copula quantile regression-based CoVaR model. We find that (i) the dynamic risk spillovers show heterogeneity over time, specifically that its downward trend is significant after the stock market disaster in 2015; (ii) JSCBs display the largest risk spillovers, indicating that JSCBs are the main contributors to systemic risk in China’s financial system; and (iii) the risk spillovers are not symmetrical, as the upside risk spillovers are smaller than the downside risk spillovers. Our results have crucial implications for financial regulators and investors who want to measure and prevent systemic financial risk and optimise their investment strategies.