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Computation of systemic risk measures: a mixed-integer programming approach

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  • c{C}au{g}{i}n Ararat
  • Nurtai Meimanjan

Abstract

Systemic risk is concerned with the instability of a financial system whose members are interdependent in the sense that the failure of a few institutions may trigger a chain of defaults throughout the system. Recently, several systemic risk measures have been proposed in the literature that are used to determine capital requirements for the members subject to joint risk considerations. We address the problem of computing systemic risk measures for systems with sophisticated clearing mechanisms. In particular, we consider an extension of the Rogers-Veraart network model where the operating cash flows are unrestricted in sign. We propose a mixed-integer programming problem that can be used to compute clearing vectors in this model. Due to the binary variables in this problem, the corresponding (set-valued) systemic risk measure fails to have convex values in general. We associate nonconvex vector optimization problems with the systemic risk measure and provide theoretical results related to the weighted-sum and Pascoletti-Serafini scalarizations of this problem. Finally, we test the proposed formulations on computational examples and perform sensitivity analyses with respect to some model-specific and structural parameters.

Suggested Citation

  • c{C}au{g}{i}n Ararat & Nurtai Meimanjan, 2019. "Computation of systemic risk measures: a mixed-integer programming approach," Papers 1903.08367, arXiv.org, revised Aug 2023.
    • Handle: RePEc:arx:papers:1903.08367
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    References listed on IDEAS

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    7. Larry Eisenberg & Thomas H. Noe, 2001. "Systemic Risk in Financial Systems," Management Science, INFORMS, vol. 47(2), pages 236-249, February.
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    Cited by:

    1. Giuseppe Calafiore & Giulia Fracastoro & Anton Proskurnikov, 2024. "Default Resilience and Worst-Case Effects in Financial Networks," Papers 2403.10631, arXiv.org.
    2. repec:hal:wpaper:hal-03284655 is not listed on IDEAS
    3. Lukas Gonon & Thilo Meyer-Brandis & Niklas Weber, 2024. "Computing Systemic Risk Measures with Graph Neural Networks," Papers 2410.07222, arXiv.org.

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