Pitfalls in systemic-risk scoring

Benoît, Sylvain; Hurlin, Christophe; Pérignon, Christophe

doi:10.1016/j.jfi.2018.05.004

Cited by 34 publications

(25 citation statements)

References 12 publications

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“…5. This is an interesting difference between the ESRB and the Basel Committee on Banking Supervision (BCBS), which has established the framework to identify Systematically Important Financial Institutions, see Benoit, Hurlin, and Pérignon (2019). In contrast to the ESRB, the BCBS regularly invites industry members during the meetings of the Macroprudential Supervision Group.…”

Section: Resultsmentioning

confidence: 99%

The European Systemic Risk Board – governance and early experience

Ehrmann

Schure

2019

Journal of Economic Policy Reform

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The European Systemic Risk Board is charged with the macroprudential oversight of the financial system in the European Union. We compare and contrast the ESRB with the U.S. National Transportation Safety Board, which some scholars proposed as a role model for systemic risk management. There are parallels and some differences between these organisations. Neither institution has direct regulatory power, which could paradoxically be beneficial as it may help preserve their independence and objectivity in the long run. We also review the ESRB's activities after it started in 2010 and assess the effect of its first public recommendation.

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Section: Resultsmentioning

confidence: 99%

The European Systemic Risk Board – governance and early experience

Ehrmann

Schure

2019

Journal of Economic Policy Reform

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“…For further details, see Basel Committee on Banking Supervision (2014). tests, and Benoit et al (2019) identified some pitfalls in the systemic risk scoring. The second set of systemic risk measures relies on high-frequency market data such as stocks or asset returns, option prices, or CDS spreads.…”

Section: Introductionmentioning

confidence: 99%

Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures

et al. 2019

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This paper proposes an original approach for backtesting systemic risk measures. This backtesting approach makes it possible to assess the systemic risk measure forecasts used to identify the financial institutions that contribute the most to the overall risk in the financial system. Our procedure is based on simple tests similar to those generally used to backtest the standard market risk measures such as value-at-risk or expected shortfall. We introduce a concept of violation associated with the marginal expected shortfall (MES), and we define unconditional coverage and independence tests for these violations. We can generalize these tests to any MES-based systemic risk measures such as SES, SRISK, or ∆CoVaR. We study their asymptotic properties in the presence of estimation risk and investigate their finite sample performance via Monte Carlo simulations. An empirical application to a panel of U.S. financial institutions is conducted to assess the validity of MES, SRISK, and ∆CoVaR forecasts issued from a GARCH-DCC model. Our results show that this model provides valid forecasts for MES and SRISK when considering a medium-term horizon. Finally, we propose an early warning system indicator for future systemic crises deduced from these backtests. Our indicator quantifies how much is the measurement error issued by a systemic risk forecast at a given point in time which can serve for the early detection of global market reversals.

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“…Additionally and for the portfolio allocation, we used MES because, under the assumption that the individual financial institution and the index returns are driven by a bivariate GARCH, the MES of the financial institution is proportional to its systematic risk, as measured by its time-varying beta (see Benoit et al 2017). On the other hand, there is much criticism of the systemic risk measures, and various papers have presented their shortcomings (among others, Danielsson et al 2016;Benoit et al 2019;Idier et al 2014). The discussion of the adequacy of the systemic risk measures is out of the scope of this paper, and we chose to use MES and its two alternatives, for three main reasons: First, the stock returns used for its calculation are easily obtained and updated, contrary to the balance sheet data used in other measures that are updated at a quarterly frequency.…”

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confidence: 99%

The Leaders, the Laggers, and the “Vulnerables”

Arakelian

Hashem

2020

Risks

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We examine the lead-lag effect between the large and the small capitalization financial institutions by constructing two global weekly rebalanced indices. We focus on the 10% of stocks that “survived” all the rebalancings by remaining constituents of the indices. We sort them according to their systemic importance using the marginal expected shortfall (MES), which measures the individual institutions’ vulnerability over the market, the network based MES, which captures the vulnerability of the risks generated by institutions’ interrelations, and the Bayesian network based MES, which takes into account different network structures among institutions’ interrelations. We also check if the lead-lag effect holds in terms of systemic risk implying systemic risk transmission from the large to the small capitalization, concluding a mixed behavior compared to the index returns. Additionally, we find that all the systemic risk indicators increase their magnitude during the financial crisis.

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Pitfalls in systemic-risk scoring

Cited by 34 publications

References 12 publications

The European Systemic Risk Board – governance and early experience

The European Systemic Risk Board – governance and early experience

Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures

The Leaders, the Laggers, and the “Vulnerables”

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