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Multi-Variate Risk Measures under Wasserstein Barycenter

Author

Listed:
  • M. Andrea Arias-Serna

    (Faculty of Engineering, University of Medellin, Medellin 050026, Colombia
    These authors contributed equally to this work.)

  • Jean Michel Loubes

    (Institut de Mathématiques de Toulouse, University of Toulouse, 31062 Toulouse, France
    These authors contributed equally to this work.)

  • Francisco J. Caro-Lopera

    (Faculty of Basic Sciences, University of Medellin, Medellin 050026, Colombia
    These authors contributed equally to this work.)

Abstract

When the uni-variate risk measure analysis is generalized into the multi-variate setting, many complex theoretical and applied problems arise, and therefore the mathematical models used for risk quantification usually present model risk. As a result, regulators have started to require that the internal models used by financial institutions are more precise. For this task, we propose a novel multi-variate risk measure, based on the notion of the Wasserstein barycenter. The proposed approach robustly characterizes the company’s exposure, filtering the partial information available from individual sources into an aggregate risk measure, providing an easily computable estimation of the total risk incurred. The new approach allows effective computation of Wasserstein barycenter risk measures in any location–scatter family, including the Gaussian case. In such cases, the Wasserstein barycenter Value-at-Risk belongs to the same family, thus it is characterized just by its mean and deviation. It is important to highlight that the proposed risk measure is expressed in closed analytic forms which facilitate its use in day-to-day risk management. The performance of the new multi-variate risk measures is illustrated in United States market indices of high volatility during the global financial crisis (2008) and during the COVID-19 pandemic situation, showing that the proposed approach provides the best forecasts of risk measures not only for “normal periods”, but also for periods of high volatility.

Suggested Citation

  • M. Andrea Arias-Serna & Jean Michel Loubes & Francisco J. Caro-Lopera, 2022. "Multi-Variate Risk Measures under Wasserstein Barycenter," Risks, MDPI, vol. 10(9), pages 1-15, September.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:9:p:180-:d:909068
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    References listed on IDEAS

    as
    1. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
    2. Jianping Li & Xiaoqian Zhu & Cheng-Few Lee & Dengsheng Wu & Jichuang Feng & Yong Shi, 2015. "On the aggregation of credit, market and operational risks," Review of Quantitative Finance and Accounting, Springer, vol. 44(1), pages 161-189, January.
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