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Systemic Risk Exposures: A 10-by-10-by-10 Approach

In: Risk Topography: Systemic Risk and Macro Modeling

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  • Darrell Duffie

Abstract

Here, I present and discuss a "10-by-10-by-10" network-based approach to monitoring systemic financial risk. Under this approach, a regulator would analyze the exposures of a core group of systemically important financial firms to a list of stressful scenarios, say 10 in number. For each scenario, about 10 such designated firms would report their gains or losses. Each reporting firm would also provide the identities of the 10, say, counterparties with whom the gain or loss for that scenario is the greatest in magnitude relative to all counterparties. The gains or losses with each of those 10 counterparties would also be reported, scenario by scenario. Gains and losses would be measured in terms of market value and also in terms of cash flow, allowing regulators to assess risk magnitudes in terms of stresses to both economic values and also liquidity. Exposures would be measured before and after collateralization. One of the scenarios would be the failure of a counterparty. The "top ten" counterparties for this scenario would therefore be those whose defaults cause the greatest losses to the reporting firm. In eventual practice, the number of reporting firms, the number of stress scenarios, and the number of major counterparties could all exceed 10, but it is reasonable to start with a small reporting system until the approach is better understood and agreed upon internationally.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Darrell Duffie, 2013. "Systemic Risk Exposures: A 10-by-10-by-10 Approach," NBER Chapters, in: Risk Topography: Systemic Risk and Macro Modeling, pages 47-56, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberch:12512
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    Cited by:

    1. Mr. Manmohan Singh, 2012. "Puts in the Shadow," IMF Working Papers 2012/229, International Monetary Fund.
    2. Greenwood, Robin & Landier, Augustin & Thesmar, David, 2015. "Vulnerable banks," Journal of Financial Economics, Elsevier, vol. 115(3), pages 471-485.
    3. Borio, Claudio & Drehmann, Mathias & Tsatsaronis, Kostas, 2014. "Stress-testing macro stress testing: Does it live up to expectations?," Journal of Financial Stability, Elsevier, vol. 12(C), pages 3-15.
    4. Marc Busse & Michel Dacorogna & Marie Kratz, 2014. "The Impact of Systemic Risk on the Diversification Benefits of a Risk Portfolio," Risks, MDPI, vol. 2(3), pages 1-17, July.
    5. Varotto, Simone & Zhao, Lei, 2018. "Systemic risk and bank size," Journal of International Money and Finance, Elsevier, vol. 82(C), pages 45-70.
    6. Beteto Wegner, Danilo Lopomo, 2014. "Network Formation and Financial Fragility," Risk and Sustainable Management Group Working Papers 179222, University of Queensland, School of Economics.
    7. Viral V. Acharya & Matthew Richardson, 2012. "Implications of the Dodd-Frank Act," Annual Review of Financial Economics, Annual Reviews, vol. 4(1), pages 1-38, October.
    8. Beteto, Danilo Lopomo, 2012. "Government Intervention and Financial Fragility," Risk and Sustainable Management Group Working Papers 156477, University of Queensland, School of Economics.
    9. Ho Hwang, Jong, 2014. "A proposal for an open-source financial risk model," LSE Research Online Documents on Economics 59298, London School of Economics and Political Science, LSE Library.
    10. Thomas Breuer & Martin Summer, 2013. "Stress Test Robustness: Recent Advances and Open Problems," Financial Stability Report, Oesterreichische Nationalbank (Austrian Central Bank), issue 25, pages 74-86.

    More about this item

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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