A stable systemic risk ranking in China’s banking sector: Based on principal component analysis

Fang, Libing; Xiao, Binqing; H, Yu; You, Qixing

doi:10.1016/j.physa.2017.11.115

Cited by 27 publications

(13 citation statements)

References 34 publications

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“…The result is very controversial compared to our expectation and with the other risk measures pattern. However, it is in line with Nucera et al (2016) and Fang et al (2018), that find the similarities stable loadings for the US market and China financial sectors, respectively. Obviously, the peaks are periods of crisis, but at the same time, we see an evident change in behaviour after 2012.…”

Section: T3‐pca Ranking Analysissupporting

confidence: 87%

“…The findings support that these systemic risk measures cannot correctly identify the most systemically important financial institutions over a certain period. This confirms the criticism of Benoit et al (2013), Danielsson et al (2016) and Benoit et al (2017), that document how these individual measures of systemic risk depend on the extreme distribution of stock return and, which in turn, make heterogeneous the bank ranking (Fang et al, 2018; Nucera et al, 2016).…”

Section: An Overview Of Systemic Risk In Eurolandsupporting

confidence: 79%

“…The combined ranking is constructed from the A‐mode eigenvalue that explains most of the variance of the observed data. Different to the work of Nucera et al (2016) and Fang et al (2018) that use principal components only in the cross‐section direction, our paper estimates all factors also in a time series context (C‐mode). Our sample is composed of major listed Eurozone banks.…”

Section: Introductionmentioning

confidence: 93%

“…Although these types of market indicators contain a large set of information about systemic risk (Fang, Xiao, Yu, & You, 2018), they are not capable of identifying a reliable bank ranking consistently and stably. By factor analysis—as an information aggregation tool—we can resolve this problem.…”

Section: Introductionmentioning

confidence: 99%

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The triple (T3) dimension of systemic risk: Identifying systemically important banks

Foglia

Angelini

2020

Int. J Fin Econ

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This paper proposes a new systemic risk measure based on a multi‐way analysis. The systemic risk is composed of two different components: the time and the cross‐dimension. The first refers to the accumulation of banking risk and its interaction with the business cycle, while the second concern, the high‐level concentration of the specific risk on relevant financial institutions. Thanks to the three‐way analysis, we can, on the one hand, estimate a new measure of systemic risk based on these both components, and, on the other hand, we can transparently identify the SIBs (systemically important banks). To this end, we have empirically evaluated and compared the marginal expected shortfall (MES), the SRISK measure and the conditional value at risk (CoVaR) based on a representative sample of Eurozone institutions listed on the stock exchange for the period from June 2005 to May 2018. Our results show how these systemic risk measures produce different systemic risk classifications for the same bank. The findings, therefore, highlight the fragility and structural dependence of these measures, which may not be used for the estimation of a stable rank. Applying three‐way factorial analysis, we show how our measure gives a more stable score. Moreover, our index is the first one to be composed of both the cross‐section and the temporal components, essential elements for a proper assessment of systemic risk.

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Section: T3‐pca Ranking Analysissupporting

confidence: 87%

Section: An Overview Of Systemic Risk In Eurolandsupporting

confidence: 79%

Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The triple (T3) dimension of systemic risk: Identifying systemically important banks

Foglia

Angelini

2020

Int. J Fin Econ

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“…Our study contributes to the academic literature on systemic risk in the Chinese financial system. To name a few, Huang et al (2017) and Fang et al (2018) study the systemic risk ranking of Chinese banks under various measures (e.g., the CoVaR and the systemic risk measure (SRISK) approaches). Wang et al (2018) investigate the interconnectedness and systemic risk of Chinese financial institutions by constructing dynamic tail-event driven networks.…”

Section: Introductionmentioning

confidence: 99%

Systemic Risk in the Chinese Stock Market Under Different Regimes: A Sector-Level Perspective

Yang

Cheng

Huang

et al. 2019

Asian Economic and Financial Review

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This study's aim is to investigate systemic risk in the Chinese stock market. To this end, we analyze risk contributions to the Chinese stock market from 2007 to 2018 at the sector level using the Conditional Value at Risk (CoVaR) approach proposed by Adrian and Brunnermeier (2016). For the full sample period, we find that the information technology sector is the top contributor to systemic risk in the Chinese stock market. To distinguish the risk contribution of each sector under different market regimes, we propose an adjusted Bry-Boschan program to identify turning points in the stock market, which captures regime shifting between bull and bear markets. We find that the risk contribution of each sector in a bear market is significantly higher than that in the following bull market. We also find that the top contributor to systemic risk in the Chinese stock market changes across market regimes. Our findings have important policy implications. First, policymakers may use the early identification of systemically risky sectors of the stock market to improve the pertinence of economic policy-making. Second, it may allow security regulators to foster an environment in which incentives for risk taking by financial practitioners are reduced. Contribution/ Originality:This study is one of very few studies which have investigated the systemic risk in the stock market by employing the systemic risk method to estimate ten stock sectors' risk contributions to the Chinese stock market.

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Sectoral portfolio optimization by judicious selection of financial ratios via PCA

Dhingra,

Sharma,

Gupta

2023

Optim Eng

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A stable systemic risk ranking in China’s banking sector: Based on principal component analysis

Cited by 27 publications

References 34 publications

The triple (T3) dimension of systemic risk: Identifying systemically important banks

The triple (T3) dimension of systemic risk: Identifying systemically important banks

Systemic Risk in the Chinese Stock Market Under Different Regimes: A Sector-Level Perspective

Sectoral portfolio optimization by judicious selection of financial ratios via PCA

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