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Reverse stress testing interbank networks

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  • Grigat, Daniel
  • Caccioli, Fabio

Abstract

We reverse engineer dynamics of financial contagion to find the scenario of smallest exogenous shock that, should it occur, would lead to a given final systemic loss. This reverse stress test can be used to identify the potential triggers of systemic events, and it removes the arbitrariness in the selection of shock scenarios in stress testing. We consider in particular the case of distress propagation in an interbank market, and we study a network of 44 European banks, which we reconstruct using data collected from banks statements. By looking at the distribution across banks of the size of smallest exogenous shocks we rank banks in terms of their systemic importance, and we show the effectiveness of a policy with capital requirements based on this ranking. We also study the properties of smallest exogenous shocks as a function of the parameters that determine the endogenous amplification of shocks. We find that the size of smallest exogenous shocks reduces and that the distribution across banks becomes more localized as the system becomes more unstable.

Suggested Citation

  • Grigat, Daniel & Caccioli, Fabio, 2017. "Reverse stress testing interbank networks," LSE Research Online Documents on Economics 86746, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:86746
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    File URL: http://eprints.lse.ac.uk/86746/
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    Cited by:

    1. Chang Liu & Lin Tang & Dongtao Lin & Jiayi Guo, 2023. "Testing to extreme: An application of reverse stress testing engineering on mortgages of commercial banks in China," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 187-192, January.
    2. Ramadiah, Amanah & Fricke, Daniel & Caccioli, Fabio, 2022. "Backtesting macroprudential stress tests," Journal of Economic Dynamics and Control, Elsevier, vol. 137(C).
    3. Tabak, Benjamin Miranda & Silva, Thiago Christiano & Fiche, Marcelo Estrela & Braz, Tércio, 2021. "Citation likelihood analysis of the interbank financial networks literature: A machine learning and bibliometric approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).

    More about this item

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • F3 - International Economics - - International Finance
    • G3 - Financial Economics - - Corporate Finance and Governance

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