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Stress Testing and Systemic Risk Measures Using Elliptical Conditional Multivariate Probabilities

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  • Tomaso Aste

    (Department of Computer Science, University College London, Gower Street, London WC1E 6EA, UK
    UCL Centre for Blockchain Technologies, University College London, London WC1E 6BT, UK
    Systemic Risk Centre, London School of Economics and Political Sciences, London WC2A 2AE, UK)

Abstract

Systemic risk, in a complex system with several interrelated variables, such as a financial market, is quantifiable from the multivariate probability distribution describing the reciprocal influence between the system’s variables. The effect of stress on the system is reflected by the change in such a multivariate probability distribution, conditioned to some of the variables being at a given stress’ amplitude. Therefore, the knowledge of the conditional probability distribution function can provide a full quantification of risk and stress propagation in the system. However, multivariate probabilities are hard to estimate from observations. In this paper, I investigate the vast family of multivariate elliptical distributions, discussing their estimation from data and proposing novel measures for stress impact and systemic risk in systems with many interrelated variables. Specific examples are described for the multivariate Student-t and the multivariate normal distributions applied to financial stress testing. An example of the US equity market illustrates the practical potentials of this approach.

Suggested Citation

  • Tomaso Aste, 2021. "Stress Testing and Systemic Risk Measures Using Elliptical Conditional Multivariate Probabilities," JRFM, MDPI, vol. 14(5), pages 1-17, May.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:5:p:213-:d:551263
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    References listed on IDEAS

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