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SAFE: An early warning system for systemic banking risk

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  • Mikhail V. Oet
  • Ryan Eiben
  • Timothy Bianco
  • Dieter Gramlich
  • Stephen J. Ong
  • Jing Wang

Abstract

This paper builds on existing microprudential and macroprudential early warning systems (EWSs) to develop a new, hybrid class of models for systemic risk, incorporating the structural characteristics of the fi nancial system and a feedback amplification mechanism. The models explain fi nancial stress using both public and proprietary supervisory data from systemically important institutions, regressing institutional imbalances using an optimal lag method. The Systemic Assessment of Financial Environment (SAFE) EWS monitors microprudential information from the largest bank holding companies to anticipate the buildup of macroeconomic stresses in the financial markets. To mitigate inherent uncertainty, SAFE develops a set of medium-term forecasting specifi cations that gives policymakers enough time to take ex-ante policy action and a set of short-term forecasting specifications for verification and adjustment of supervisory actions. This paper highlights the application of these models to stress testing, scenario analysis, and policy.

Suggested Citation

  • Mikhail V. Oet & Ryan Eiben & Timothy Bianco & Dieter Gramlich & Stephen J. Ong & Jing Wang, 2011. "SAFE: An early warning system for systemic banking risk," Working Paper 1129, Federal Reserve Bank of Cleveland.
  • Handle: RePEc:fip:fedcwp:1129
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    Cited by:

    1. Aikman, David & Kiley, Michael & Lee, Seung Jung & Palumbo, Michael G. & Warusawitharana, Missaka, 2017. "Mapping heat in the U.S. financial system," Journal of Banking & Finance, Elsevier, vol. 81(C), pages 36-64.
    2. Antunes, António & Bonfim, Diana & Monteiro, Nuno & Rodrigues, Paulo M.M., 2018. "Forecasting banking crises with dynamic panel probit models," International Journal of Forecasting, Elsevier, vol. 34(2), pages 249-275.
    3. Peltonen, Tuomas & Sarlin, Peter & Piloiu, Andreea, 2015. "Network linkages to predict bank distress," Working Paper Series 1828, European Central Bank.
    4. Simone Varotto & Lei Zhao, 2014. "Systemic Risk and Bank Size," ICMA Centre Discussion Papers in Finance icma-dp2014-17, Henley Business School, Reading University.
    5. Huotari, Jarkko, 2015. "Measuring financial stress – A country specific stress index for Finland," Research Discussion Papers 7/2015, Bank of Finland.
    6. Oet, Mikhail V. & Gramlich, Dieter & Sarlin, Peter, 2016. "Evaluating measures of adverse financial conditions," Journal of Financial Stability, Elsevier, vol. 27(C), pages 234-249.
    7. Vdovychenko, Artem & Oros, Galyna, 2014. "Financial stress index: estimation and application in empirical researches in Ukraine," MPRA Paper 69337, University Library of Munich, Germany.
    8. repec:eee:finsta:v:35:y:2018:i:c:p:226-241 is not listed on IDEAS
    9. Chau, Frankie & Deesomsak, Rataporn, 2014. "Does linkage fuel the fire? The transmission of financial stress across the markets," International Review of Financial Analysis, Elsevier, vol. 36(C), pages 57-70.
    10. Domenico Di Gangi & Fabrizio Lillo & Davide Pirino, 2015. "Assessing systemic risk due to fire sales spillover through maximum entropy network reconstruction," Papers 1509.00607, arXiv.org.

    More about this item

    Keywords

    Systemic risk ; Liquidity (Economics);

    JEL classification:

    • 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
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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