Jump volatility spillover network based measurement of systemic importance of Chinese financial institutions

Xin, Yang; Shan, Chen; Hong, Liu; Yang, Xibei; Huang, Chuangxia

doi:10.1002/ijfe.2470

Cited by 26 publications

(7 citation statements)

References 49 publications

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“…Since fnancial market data are publicly available and forward-looking, they are widely used in the study of fnancial networks and systemic risk. Related studies mainly include correlation-based networks [19][20][21], Granger causality networks [22][23][24], volatility spillover networks [25][26][27], and tail risk spillover networks [28,29]. In addition, in order to take more risk information into account, some scholars have developed composite networks [30] and multilayer information spillover networks [31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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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“…Second, in contrast to the volatility spillover literature (Diebold and Yılmaz, 2014;Brunnermeier et al, 2020;Gong, et al, 2020;Yang et al, 2020), we focus on tail volatility and identify the spillover effects of various market states. Third, unlike the literature on jump volatility (Jung and Maderitsch, 2014;Lahaye and Neely, 2020;Yang et al, 2021;Huang et al, 2022), we make two significant contributions: 1. Jump volatility includes risk information only during periods of abrupt change, whereas tail volatility in this paper also includes tail volatility information during smooth periods.…”

Section: Introductionmentioning

confidence: 99%

Will a boom be followed by crash? A new systemic risk measure based on right-tail risk

Liu

Xu²,

Xiong³

2023

Front. Psychol.

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In this study, we demonstrate that high short-term gains on the A-share market may lead to significant losses in the future and potentially cause a market catastrophe. To study the accumulation, outbreak, and cross-sector spillover process of systemic risk in the Chinese stock market, we define right-tail risk as a large rally process that may lead to left-tail losses in the future and construct a tail volatility spillover network by distinguishing between left-tail and right-tail risk. In the risk accumulation process, the market expectation bias of common shocks considerably magnifies heterogeneity risk, and in the risk outbreak and spillover processes, the greatest systemically important and systemically susceptible sectors are banking and information technology, respectively. In addition, the level of risk spillover is extremely sensitive to tail shocks and increases as tail shocks intensify. Moreover, right-tail risk has more forward-looking predictive power for left-tail risk. Apart from achieving immediate regulatory objectives, Chinese authorities must consider market expectation bias when implementing rules. Additionally, authorities want to be wary of right-tail risk, which has the potential to create serious and pervasive damage in the future if the market is allowed to be unregulated during short-term spikes.

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“…Te impact of major public events on the connectedness of the international stock market has caught the attention of scholars because such events trigger frequent turbulence in the fnancial system. For example, Yang et al [39] build a jump volatility spillover network of Chinese fnancial institutions using the VAR model and fnd that network density reaches a peak during China's stock market disaster of 2015. Huang et al [40] detect the time-varying comovement among individual stocks in both normal and crisis periods based on a directed weighted stock network and a weighted LeaderRank algorithm; their results demonstrate that network density, the average clustering coefcient, and global efciency can provide an "early warning" of potential future crises.…”

Section: Introductionmentioning

confidence: 99%

Connectedness of International Stock Market at Major Public Events: Empirical Study via Dynamic Time Warping-Based Network

Xie

Ouyang

et al. 2023

Complexity

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Several public events have drawn renewed attention to the connectedness of the international stock market since the financial crisis of 2008. We investigate systemic and regional connectedness among stock markets around the world at major public events by constructing correlation networks for 46 markets based on the dynamic time-warping method. We find that (i) geographic regionalization is typically observed in the stock market network, in which France is dominant, (ii) Europe has the greatest and the Middle East and Africa the least within-region connectedness, (iii) the correlation network structure is highly integrated and compact at major public events, and global events influence the international stock market more significantly than regional events do, and (iv) the importance of China reaches its peak during the era of Sino-US trade friction, showing that public events have enormous impacts on the countries involved.

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Jump volatility spillover network based measurement of systemic importance of Chinese financial institutions

Cited by 26 publications

References 49 publications

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

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

Will a boom be followed by crash? A new systemic risk measure based on right-tail risk

Connectedness of International Stock Market at Major Public Events: Empirical Study via Dynamic Time Warping-Based Network

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