A Theoretical and Empirical Comparison of Systemic Risk Measures: MES versus CoVaR

Benoît, Sylvain; Colletaz, Gilbert; Hurlin, Christophe; Pérignon, Christophe

doi:10.2139/ssrn.1973950

Cited by 122 publications

(117 citation statements)

References 33 publications

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“…5 While many other systemic risk measures exist, our sample of risk measures 1-6 is a reasonably complete set of market based measures that regulators may look at in practice. The set nests the measures investigated by Benoit et al (2013). All risk rankings are at a monthly frequency.…”

Section: Data Sourcesmentioning

confidence: 99%

“…They demonstrate that, given a certain set of assumptions, several popular systemic risk measures can be formulated as (non-linear) functions of market risk measures such as the CAPM beta. In an earlier empirical investigation of U.S. financial firms (Benoit et al (2013)), they note that a one-factor linear model appears to explain most of the variability across four systemic risk measures (MES, SES, SRISK, and ∆CoVaR). Our paper is different in that they stop short of pursuing that intuition further by extracting the common variation across systemic risk measures to obtain a combined ranking that is less affected by model risk and estimation uncertainty (see also Danielson et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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The information in systemic risk rankings

Nucera

Schwaab

Koopman

et al. 2016

Journal of Empirical Finance

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractWe propose to pool alternative systemic risk rankings for financial institutions using the method of principal components. The resulting overall ranking is less affected by estimation uncertainty and model risk. We apply our methodology to disentangle the common signal and the idiosyncratic components from a selection of key systemic risk rankings that are recently proposed. We use a sample of 113 listed financial sector firms in the European Union over the period 2002-2013. The implied ranking from the principal components is less volatile than most individual risk rankings and leads to less turnover among the top ranked institutions. We also find that price-based rankings and fundamentals based rankings deviated substantially and for a prolonged time in the period leading up to the financial crisis. We test the adequacy of our newly pooled systemic risk ranking by relating it to credit default swap premia.

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Section: Data Sourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The information in systemic risk rankings

Nucera

Schwaab

Koopman

et al. 2016

Journal of Empirical Finance

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“…A theoretical and empirical comparison of both MES and ΔCoVaR is given by Benoit et al (2013) who show that while ΔCoVaR is the method of choice for ranking systemically important institutions, MES should be used for forecasting the contribution of a particular institution to the global risk of the financial system. and the three-month Treasury bill rate, the change in the credit spread between BAA-rated bonds and the Treasury bill rate, the return on the Case-Shiller Home Price Index, and implied equity market volatility from VIX as state variables.…”

mentioning

confidence: 99%

Why do some insurers become systemically relevant?

Weiß

Mühlnickel

2014

Journal of Financial Stability

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Are some insurers relevant for the stability of the financial system? And if yes, what firm fundamentals and aspects of insurers' business models cause them to destabilize an entire financial sector? We find that several insurers did indeed contribute significantly to the instability of the U.S. financial sector during the recent financial crisis. We empirically confirm that insurers that were most exposed to systemic risk were larger, relied more heavily on non-policyholder liabilities and had higher ratios of investment income to net revenues on average. Contrary to current conjectures of insurance regulators, we find that the contribution of insurers to systemic risk is only driven by insurer size.

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“…In contrast to previous work, we focus on the value-added of the network based measures compared to the most popular non-network based alternatives. We use the analysis of Benoit et al (2013) to reduce the number of relevant alternatives to two: beta times the market value, and value at risk (or volatility). Benoit et al (2013) show analytically that in a stylized setting the most popular risk ranking measures (MES, SES, SRISK and ∆CoVaR) can be characterised by beta and Value-at-Risk (VaR); see also Adams et al (2014) and White et al (2015).…”

Section: Srisk Ofmentioning

confidence: 99%

Network, market, and book-based systemic risk rankings

Leur

Lucas

Seeger

2017

Journal of Banking & Finance

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractWe investigate the information content of stock correlation based network measures for systemic risk rankings, such as SIFIRank (based on Google's PageRank). Using European banking data, we first show that SIFIRank is empirically equivalent to a ranking based on average pairwise stock correlations. Next, we find that correlation based network measures still appear to complement currently available systemic risk ranking methods based on book or market values. A further analytical investigation, however, shows that the value-added appears to be mainly attributable to pairwise cross-sectional heterogeneity rather than to more subtle network relations and feedback loops.

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A Theoretical and Empirical Comparison of Systemic Risk Measures: MES versus CoVaR

Cited by 122 publications

References 33 publications

The information in systemic risk rankings

The information in systemic risk rankings

Why do some insurers become systemically relevant?

Network, market, and book-based systemic risk rankings

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