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What is the information content of the SRISK measure as a supervisory tool?

Author

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  • S. Tavolaro
  • F. Visnovsky

Abstract

The SRISK measure is advertised as measuring the recapitalization needed by a financial institution in the event of a financial crisis. It is computed from the estimated reaction of the institution’s share price in the event of a sharp drop in market prices. This indicator relies both on an economic analysis and an econometric model. It is applied to a large set of international and domestic financial institutions, updated regularly and made available online. Although innovative, it stirred naturally debates among academics, supervisors and professionals, highlighting some limitations, in particular when considering the SRISK measure as a supervisory tool. First, the SRISK is based on market return data: consequently, it applies only to listed institutions and is exposed to criticisms as to which extent it can mirror fundamentals. Second, the SRISK seems to lack sound foundations for policy analysis: with a reduced-form approach, conclusions regarding causality are not obvious from an economic point of view. Moreover the SRISK is a conditional measure to an event whose likelihood is not integrated in the framework. Third, empirical analyses of SRISK as a supervisory tool, used for instance to identify systemic financial institutions (SIFIs) or as an early-warning indicator, have shown some limited perspectives.

Suggested Citation

  • S. Tavolaro & F. Visnovsky, 2014. "What is the information content of the SRISK measure as a supervisory tool?," Débats économiques et financiers 10, Banque de France.
    • Handle: RePEc:bfr:decfin:10
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    Cited by:

    1. Guillaume Arnould & Catherine Bruneau & Zhun Peng, 2015. "Liquidity and Equity Short term fragility: Stress-tests for the European banking system," Documents de travail du Centre d'Economie de la Sorbonne 15090, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    2. Somnath Chatterjee & Marea Sing, 2021. "Measuring Systemic Risk in South African Banks," Working Papers 11004, South African Reserve Bank.
    3. Henri Fraisse & Christophe Hurlin, 2024. "Modèles internes des banques pour le calcul du capital réglementaire (IRB) et intelligence artificielle," Débats économiques et financiers 44, Banque de France.
    4. Stéphane Loisel, 2014. "Reevaluation of the capital charge in insurance after a large shock: empirical and theoretical views," Post-Print hal-02013669, HAL.
    5. Cyril Pouvelle., 2022. "An Analysis of Financial Conglomerate Resilience: A Perspective on bancassurance in France [Une analyse de la résilience des conglomérats financiers : Une perspective sur la bancassurance en France," Débats économiques et financiers 39, Banque de France.
    6. Dissem, Sonia & Lobez, Frederic, 2020. "Correlation between the 2014 EU-wide stress tests and the market-based measures of systemic risk," Research in International Business and Finance, Elsevier, vol. 51(C).
    7. Denisa Banulescu-Radu & Christophe Hurlin & Jérémy Leymarie & Olivier Scaillet, 2021. "Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures," Management Science, INFORMS, vol. 67(9), pages 5730-5754, September.
    8. Robert McKeown, 2017. "How Vulnerable Is The Canadian Banking System To Fire-sales?," Working Paper 1381, Economics Department, Queen's University.
    9. Eric Monnet, & Angelo Riva, & Stefano Ungaro., 2021. "The Real Effects of Bank Runs. Evidence from the French Great Depression (1930-1931) [Les effets réels des ruées bancaires : l’exemple de la Grande Dépression en France (1930-1931)]," Débats économiques et financiers 37, Banque de France.
    10. Olivier de Bandt & Jean-Cyprien Héam & Claire Labonne & Santiago Tavolaro, 2015. "La mesure du risque systémique après la crise financière," Revue économique, Presses de Sciences-Po, vol. 66(3), pages 481-500.
    11. J. Hombert & V. Lyonnet, 2017. "Intergenerational Risk Sharing in Life Insurance: Evidence from France," Débats économiques et financiers 30, Banque de France.
    12. B. Camara & F.-D. Castellani & H. Fraisse & L. Frey & C. Héam & L. Labonne & V. Martin, 2015. "MERCURE : A Macroprudential Stress Testing Model developed at the ACPR," Débats économiques et financiers 19, Banque de France.

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    More about this item

    Keywords

    Systemic Risk Measures; Market Data; Financial Monitoring.;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation
    • G01 - Financial Economics - - General - - - Financial Crises
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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