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Deconstructing Systemic Risk: A Reverse Stress Testing Approach

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

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  • Javier Ojea-Ferreiro

    (Joint Research Centre of the European Commission (JRC)
    Complutense Institute of Economic Analysis, Quantitative Economics Department 28223)

Abstract

This chapter proposes a methodology based on a reverse stress test exercise to shed light on key questions related to systemic risk like the quantification of the losses in an adverse scenario, its probability of occurrence and the role of main contributors. We combine several measures implied by the Expected Shortfall to get time series and cross section information regarding systemic risk. We explore how these results could change depending on key parameters in a Gaussian framework.

Suggested Citation

  • Javier Ojea-Ferreiro, 2021. "Deconstructing Systemic Risk: A Reverse Stress Testing Approach," Springer Books, in: Marco Corazza & Manfred Gilli & Cira Perna & Claudio Pizzi & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 369-375, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-78965-7_54
    DOI: 10.1007/978-3-030-78965-7_54
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    References listed on IDEAS

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    1. Banulescu, Georgiana-Denisa & Dumitrescu, Elena-Ivona, 2015. "Which are the SIFIs? A Component Expected Shortfall approach to systemic risk," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 575-588.
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    4. Salleo, Carmelo & Homar, Timotej & Kick, Heinrich, 2016. "Making sense of the EU wide stress test: a comparison with the SRISK approach," Working Paper Series 1920, European Central Bank.
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    7. Matteo Burzoni & Ilaria Peri & Chiara Maria Ruffo, 2016. "On the properties of the Lambda value at risk: robustness, elicitability and consistency," Papers 1603.09491, arXiv.org, revised Feb 2017.
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    9. Ming-Yuan Leon Li & Hsiou-wei William Lin, 2004. "Estimating value-at-risk via Markov switching ARCH models - an empirical study on stock index returns," Applied Economics Letters, Taylor & Francis Journals, vol. 11(11), pages 679-691.
    10. Sylvain Benoît & Gilbert Colletaz & Christophe Hurlin & Christophe Pérignon, 2013. "A Theoretical and Empirical Comparison of Systemic Risk Measures," Working Papers halshs-00746272, HAL.
    11. Javier Ojea Ferreiro, 2018. "Contagion spillovers between sovereign and financial European sector from a Delta CoVaR approach," Documentos de Trabajo del ICAE 2018-12, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
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    15. Asmerilda Hitaj & Cesario Mateus & Ilaria Peri, 2018. "Lambda Value at Risk and Regulatory Capital: A Dynamic Approach to Tail Risk," Risks, MDPI, vol. 6(1), pages 1-18, March.
    16. Huaizhi Chen & Lauren Cohen & Umit Gurun, 2019. "Don’t Take Their Word For It: The Misclassification of Bond Mutual Funds," NBER Working Papers 26423, National Bureau of Economic Research, Inc.
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    More about this item

    Keywords

    Systemic risk; Financial sector; Expected shortfall; Conditional measures;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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