Analysis of banks’ systemic risk contribution and contagion determinants through the leave-one-out approach

Zedda, Stefano; Cannas, Giuseppina

doi:10.1016/j.jbankfin.2017.06.008

Cited by 41 publications

(25 citation statements)

References 82 publications

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“…The difference between these two measures is the contribution of the removed bank to the systemic risk of the system. Note that as underlined by Zedda and Cannas (2017), the LOO methodology has some similarities to the Shapley value (Shapley, 1953), which is used by many authors to measure systemic risk contributions (Tarashev et al, 2010;Drehmann and Tarashev, 2013). Nonetheless, to the best of our knowledge, our paper is the first to mobilise the LOO concept for measuring systemic risk contributions using Granger-causality networks.…”

Section: Introductionmentioning

confidence: 91%

“…It is worth noting that using the LOO approach is not new in the literature on systemic risk measures. Indeed, Zedda and Cannas (2017) employ this methodology to analyse systemic risk and the determinants of contagion in a banking system. Formally, they base their approach on a simulated distribution of the losses of the entire system, and of each subsystem in which one bank was removed.…”

Section: Introductionmentioning

confidence: 99%

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Measuring network systemic risk contributions: A leave-one-out approach

Hué

Lucotte

Tokpavi

2019

Journal of Economic Dynamics and Control

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Granger-causality measures of interconnectedness between financial institutions are useful indicators of systemic risk, as they help in evaluating how far the distress of one institution is disseminated across the whole of the financial system through networks. This article provides a critical assessment of Granger-causality networks, showing that they can lead to inconsistent measures of systemic risk contributions because of the presence of spurious causalities arising from indirect contagion effects. The traditional solutions for controlling for these effects using inference on conditional Granger-causality lead to the curse of dimensionality though. To solve this, we provide a measure of financial network systemic risk contributions that is based on the leave-one-out (LOO) concept. For a given financial institution, the new measure evaluates how far the total number of significant Granger-causalities breaks down when this institution is excluded from the system. We control for spurious causalities between the remaining institutions due to the indirect contagion effect of the excluded financial institution using a conditional Granger-causality test, which is free of the curse of dimensionality. Empirical applications are conducted using daily market returns for a sample of the world's largest banks. The results show that our measure gives a meaningful ranking of the systemic importance of financial institutions that is consistent with the ranking of global systemically important banks (G-SIBs) provided by the Financial Stability Board (FSB). Moreover, our measure is shown to be a robust and significant early-warning indicator of large losses from a systemic event, and is strongly driven by balance-sheet variables related to size, business model and profitability.JEL Codes: G12, G29, C51

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Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Measuring network systemic risk contributions: A leave-one-out approach

Hué

Lucotte

Tokpavi

2019

Journal of Economic Dynamics and Control

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“…The empirical research on credit booms and financial crises divulge positive impact of credit expansion on financial stress (Crowe et al, 2011;Dell'Ariccia & Marquez, 2006). The relevance of credit volume is also highlighted by Zeda and Kannas (Zedda & Cannas, 2017).…”

Section: Determinants Of Systemic Riskmentioning

confidence: 99%

MES vs ∆CoVaR: Empirical evidence from Pakistan

Hanif

Yousaf

Waheed

et al. 2021

Cogent Business & Management

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The global financial crisis unveiled that inadequate analysis of risk can annihilate the financial system and repercussions can encompass the whole economy. Pakistan is one of the developing economies that has experienced robust growth in the banking sector. This hard earned growth can only be sustained by adequately examining the risk exposure of the financial system. Consistent with this purview, this study attempts to comprehensively analyse for the first time, the systemic importance of financial institutions of Pakistan using ∆CoVaR and MES. Moreover, the study employs System GMM to analyze the bank, sector and country level determinants of systemic risk measures. The findings of the study signify that MES and ∆CoVaR measures identify different institutions as systemically important. Similarly, the influence of variables also changes with change in the systemic measure. The estimation of determinants of systemic risk outline that non-interst income is insignificant when MES is used as measure of systemic risk but the same turns significant for ∆CoVaR. The impact of deposit ratio also changes across the measures of systemic risk. Concentration has positive impact on MES but negatively influences ∆CoVaR. Finally, the impact of bank claims also varies across the measures of systemic risk. The study contributes to the literature by highlighting the complementary nature of systemic risk measures for the first time in a developing economy like Pakistan. The study also identifies important relationships necessary ABOUT THE AUTHORS

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“…Due to the model flexibility (then identified as systemic model of banking originated losses, or SYMBOL), it was subsequently adopted by the European Commission as a standard tool for testing and back testing the banking regulation proposals and directives. Zedda and Cannas [39] furtherly developed this approach, for measuring each financial institution's systemic risk contribution through the leave-one-out model, splitting it into the stand-alone and contagion risk factors, and analyzing the role of significant variables in the balance sheet as systemic risk determinants.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The leave-one-out model proposed by Zedda [39,42] being based on the bank's balance sheet values proved to be more accurate and appropriate for analyzing China's banking system. The basic idea behind the leave-one-out approach is that the marginal effects of the bank on systemic risk can be obtained by comparing the performance of the entire banking system to the performance of the same system excluding the bank under consideration.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of China Commercial Banks’ Systemic Risk Sustainability through the Leave-One-Out Approach

2019

Self Cite

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One of the main issues in the recent Chinese financial reform is aimed at effectively measuring systemic risk and taking appropriate measures to ensure its sustainability and prevent new crises. In this paper, we firstly introduced the present macro-prudential policies implied in China and pointed out the existing problems. Secondly, we analyzed the banks’ assets riskiness and the banks’ probability to default, then, by means of a leave-one-out model, we measured each commercial bank systemic risk contribution. Thirdly, based on comprehensive empirical results and theoretical analysis, we provided some references for macro-prudential regulation and supervision. Results show that systemic risk is increasing in 2013–2017, in particular with reference to contagion risk, with a specific concentration within joint-stock commercial banks, suggesting a specific attention of regulators and supervisors for this category.

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Analysis of banks’ systemic risk contribution and contagion determinants through the leave-one-out approach

Cited by 41 publications

References 82 publications

Measuring network systemic risk contributions: A leave-one-out approach

Measuring network systemic risk contributions: A leave-one-out approach

MES vs ∆CoVaR: Empirical evidence from Pakistan

Analysis of China Commercial Banks’ Systemic Risk Sustainability through the Leave-One-Out Approach

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