Modeling the Paths of China’s Systemic Financial Risk Contagion: A Ripple Network Perspective Analysis

Xu, Fuwei

doi:10.1007/s10614-022-10329-4

Cited by 5 publications

(6 citation statements)

References 34 publications

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“…Basic Idea of the Ripple-Spreading Network. Considering the simultaneous existence of deterministic and uncertain factors in fnancial contagion, we derive the semideterministic ripple-spreading network model (SD-RSNM) based on Hu et al [35] and Xu et al [43] to simulate fnancial contagion in China's fnancial system. In this model, there are two classes of parameters: contagion source related and network node related.…”

Section: Methodsmentioning

confidence: 99%

“…Spreading speed s i : Following Xu et al [43], we use the average turnover rate to specify s i under diferent systemic risk regimes. For example, the average turnover rate of ICBC under a high systemic risk regime is 0.081, so we set s ICBC (h) � 0.081; the average turnover rate of ICBC under a low systemic risk regime is 0.066, so we set s ICBC (l) � 0.066.…”

Section: Parameter Specifcationsmentioning

confidence: 99%

“…Given a lager enough E 0 , the ripple-spreading process will eventually form a stable fully connected network with nodes N and edges N(N − 1), which is not conducive to analyzing the systemic importance of fnancial institutions. Following Xu et al [43], we use the variance of node degrees to identify the heterogeneous network of the ripple-spreading process under diferent systemic risk regimes. For every time instant t, let A t � (a t ij ), i, j � 1, 2, • • •, N be the adjacency matrix of the instantaneous network in the ripple-spreading process.…”

Section: Heterogeneous Networkmentioning

confidence: 99%

“…Clearly, η has an important infuence on the decaying speed of ripples and will therefore afect the fnal network topology. In this paper, following Xu et al [43], we set η � 1.…”

Section: Appendixmentioning

confidence: 99%

“…For example, Su et al [42] proposed a ripple network-based collective spillover efect approach and identifed the systemic importance of fnancial markets. Xu et al [43] pointed out that the ripple efect is one of the most features of fnancial contagion and modeled the paths of China's systemic risk contagion under diferent contagion sources. However, the results are far from sufcient, and there are still some issues that need to be further explored.…”

Section: Introductionmentioning

confidence: 99%

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Ripple-Spreading Network of China’s Systemic Financial Risk Contagion: New Evidence from the Regime-Switching Model

Zhang,

Xie,

Zhou

2024

Complexity

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A better understanding of financial contagion and systemically important financial institutions (SIFIs) is essential for the prevention and control of systemic financial risk. Considering the ripple effect of financial contagion, we integrate the relevant spatiotemporal information that affects financial contagion and propose to use the ripple-spreading network to simulate the dynamic process of risk contagion in China’s financial system. In addition, we introduce the smooth-transition vector autoregression (STVAR) model to identify “high” and “low” systemic risk regimes and set the relevant parameters of the ripple-spreading network on this basis. The results show that risk ripples spread much faster in high than in low systemic risk regimes. However, systemic shocks can also trigger large-scale risk contagion in the financial system even in a low systemic risk regime as the risk ripple continues. In addition, whether the financial system is in a high or low systemic risk regime, the risk ripples from a contagion source (i.e., a real estate company) spread first to the real estate sector and the banking sector. The network centrality results of the heterogeneous ripple-spreading network indicate that most securities and banks and some real estate companies have the highest systemic importance, followed by the insurance, and finally the diversified financial institutions. Our study provides a new perspective on the regulatory practice of systemic financial risk and reminds regulators to focus not only on large institutions but also on institutions with strong ripple capacity.

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Section: Methodsmentioning

confidence: 99%

Section: Parameter Specifcationsmentioning

confidence: 99%

Section: Heterogeneous Networkmentioning

confidence: 99%

“…Clearly, η has an important infuence on the decaying speed of ripples and will therefore afect the fnal network topology. In this paper, following Xu et al [43], we set η � 1.…”

Section: Appendixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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