Identifying systemically important financial institutions in China: new evidence from a dynamic copula-CoVaR approach

Wu, Fei; Zhang, Zhiwei; Zhang, Dayong; Ji, Qiang

doi:10.1007/s10479-021-04176-z

Cited by 10 publications

(4 citation statements)

References 53 publications

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“…Compared with the other two methods, the copula model can more flexibly describe the non-linear dependence between financial markets and focuses on the characterization of the tail dependence structure (Liu et al 2023a). The further introduction of time-varying parameters captures dynamic changes in dependence relationships, thereby improving the accuracy of risk spillover measures (Wang and Xu 2022;Wu et al 2021). Yao et al (2024) examined the risk spillovers among the Chinese mainland, Hong Kong, and London stock markets using a dynamic copula-CoVaR model and found that implementing the Shanghai-Hong Kong and Shanghai-London Stock Connections enhanced the spillovers between these markets.…”

Section: Literature Reviewmentioning

confidence: 99%

Bidirectional Risk Spillovers between Chinese and Asian Stock Markets: A Dynamic Copula-EVT-CoVaR Approach

Zhao,

Park

2024

JRFM

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This study aims to investigate bidirectional risk spillovers between the Chinese and other Asian stock markets. To achieve this, we construct a dynamic Copula-EVT-CoVaR model based on 11 Asian stock indexes from 1 January 2007 to 31 December 2021. The findings show that, firstly, synchronicity exists between the Chinese stock market and other Asian stock markets, creating conditions for risk contagion. Secondly, the Chinese stock market exhibits a strong risk spillover to other Asian stock markets with time-varying and heterogeneous characteristics. Additionally, the risk spillover displays an asymmetry, indicating that the intensity of risk spillover from other Asian stock markets to the Chinese is weaker than that from the Chinese to other Asian stock markets. Finally, the Chinese stock market generated significant extreme risk spillovers to other Asian stock markets during the 2007–2009 global financial crisis, the European debt crisis, the 2015–2016 Chinese stock market crash, and the China–US trade war. However, during the COVID-19 pandemic, the risk spillover intensity of the Chinese stock market was weaker, and it acted as the recipient of risk from other Asian stock markets. The originality of this study is reflected in proposing a novel dynamic copula-EVT-CoVaR model and incorporating multiple crises into an analytical framework to examine bidirectional risk spillover effects. These findings can help Asian countries (regions) adopt effective supervision to deal with cross-border risk spillovers and assist Asian stock market investors in optimizing portfolio strategies.

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Section: Literature Reviewmentioning

confidence: 99%

Bidirectional Risk Spillovers between Chinese and Asian Stock Markets: A Dynamic Copula-EVT-CoVaR Approach

Zhao,

Park

2024

JRFM

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“…However, these methods fail to consider the interdependence between and volatility of financially relevant time-series data [3]. Therefore, some researchers have further employed time-varying generalized the auto-regressive conditional heteroscedasticity model and the rolling window dynamic Copula method to calculate indicators such as CoVaR to study systemic hazard overflows [4]. The second category involves analyzing financial hazard overflow via variance decomposition-based information overflow indicators and examining the evolving changes in hazard overflow via rolling windows [5].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Evolving Hazard Overflows and Construction of an Alert System in the Chinese Finance Industry Using Statistical Learning Methods

2023

Mathematics

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With the global economic situation still uncertain and various businesses interconnected within the finance system, financial hazards exhibit characteristics such as rapid propagation and wide scope. Therefore, it is of great significance to analyze evolving changes and patterns of hazard overflow in the finance industry and construct a financial hazard alert system. We adopt the time-varying parameter vector auto-regressive model to examine the degree and evolving characteristics of financial hazard alerts from an industry perspective and construct financial hazard measurement indicators. To effectively prevent financial hazards and consider the non-linear causal relationship between financial hazards and macroeconomic variables, we utilize the long/short-term memory network model, which can capture temporal features, to construct a financial hazard alert system. Furthermore, we explore whether the inclusion of an online sentiment indicator can enhance the accuracy of financial hazard alerts, aiming to provide policy recommendations on strengthening financial market stability and establishing a hazard alert mechanism under macro-prudential supervision.

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“…Those financial institutions that may cause systemic financial risks are defined by the Financial Stability Board (FSB) as systemically important financial institutions (SIFIs). Wu et al [ 27 ] use a copula CoVaR approach to identify SIFIs in China. The distinctive features of such financial institutions are the large scale of their operations and the high complexity of their business, which can trigger enormous shocks to regional or even global financial systems if a risk event occurs [ 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

Spatial Spillovers of Financial Risk and Their Dynamic Evolution: Evidence from Listed Financial Institutions in China

Chen

Guo

Qiang

2022

Entropy

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This paper investigates the multidimensional spatial effects of risk spillovers among Chinese financial institutions and the dynamic evolution of financial risk contagion in the tail risk correlation network over different time periods. We first measure risk spillovers from financial submarkets to the stock market, identifying five periods using structural breakpoint tests. Then, we construct a spatial error financial network panel model by combining complex network and spatial econometric theory to explore the spatial spillover variability. Finally, we calculate the Bonacich centrality of nodes in the tail risk network and analyze the dynamic evolution of the financial impact path during the different time periods. The results show that the multidimensional spatial spillovers of financial risk among financial institutions are obvious and time varying. The spatial spillovers of financial institutions are positively correlated with the turnover rate and negatively correlated with the exchange rate, interest rate and return volatility. Financial institutions of the same type in the tail risk network display intraindustry risk clustering, and the systemically important institutions identified based on Bonacich centrality differ significantly across time. Moreover, when risk spillovers increase, external shocks’ destructive power and speed of transmission to the network rise.

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Identifying systemically important financial institutions in China: new evidence from a dynamic copula-CoVaR approach

Cited by 10 publications

References 53 publications

Bidirectional Risk Spillovers between Chinese and Asian Stock Markets: A Dynamic Copula-EVT-CoVaR Approach

Bidirectional Risk Spillovers between Chinese and Asian Stock Markets: A Dynamic Copula-EVT-CoVaR Approach

Analysis of Evolving Hazard Overflows and Construction of an Alert System in the Chinese Finance Industry Using Statistical Learning Methods

Spatial Spillovers of Financial Risk and Their Dynamic Evolution: Evidence from Listed Financial Institutions in China

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