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Estimation of Systemic Shortfall Risk Measure using Stochastic Algorithms

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  • Sarah Kaakai

    (LMM)

  • Anis Matoussi

    (LMM)

  • Achraf Tamtalini

    (LMM)

Abstract

Systemic risk measures were introduced to capture the global risk and the corresponding contagion effects that is generated by an interconnected system of financial institutions. To this purpose, two approaches were suggested. In the first one, systemic risk measures can be interpreted as the minimal amount of cash needed to secure a system after aggregating individual risks. In the second approach, systemic risk measures can be interpreted as the minimal amount of cash that secures a system by allocating capital to each single institution before aggregating individual risks. Although the theory behind these risk measures has been well investigated by several authors, the numerical part has been neglected so far. In this paper, we use stochastic algorithms schemes in estimating MSRM and prove that the resulting estimators are consistent and asymptotically normal. We also test numerically the performance of these algorithms on several examples.

Suggested Citation

  • Sarah Kaakai & Anis Matoussi & Achraf Tamtalini, 2022. "Estimation of Systemic Shortfall Risk Measure using Stochastic Algorithms," Papers 2211.16159, arXiv.org, revised Feb 2024.
    • Handle: RePEc:arx:papers:2211.16159
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    Cited by:

    1. Sarah Kaakai & Anis Matoussi & Achraf Tamtalini, 2022. "Multivariate Optimized Certainty Equivalent Risk Measures and their Numerical Computation," Working Papers hal-03817818, HAL.
    2. Sarah Kaakai & Anis Matoussi & Achraf Tamtalini, 2022. "Multivariate Optimized Certainty Equivalent Risk Measures and their Numerical Computation," Papers 2210.13825, arXiv.org, revised Dec 2022.

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