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Risk Measures Estimation Under Wasserstein Barycenter

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  • M. Andrea Arias-Serna
  • Jean-Michel Loubes
  • Francisco J. Caro-Lopera

Abstract

Randomness in financial markets requires modern and robust multivariate models of risk measures. This paper proposes a new approach for modeling multivariate risk measures under Wasserstein barycenters of probability measures supported on location-scatter families. Simple and advanced copulas multivariate Value at Risk models are compared with the derived technique. The performance of the model is also checked in market indices of United States generated by the financial crisis due to COVID-19. The introduced model behaves satisfactory in both common and volatile periods of asset prices, providing realistic VaR forecast in this era of social distancing.

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  • M. Andrea Arias-Serna & Jean-Michel Loubes & Francisco J. Caro-Lopera, 2020. "Risk Measures Estimation Under Wasserstein Barycenter," Papers 2008.05824, arXiv.org.
    • Handle: RePEc:arx:papers:2008.05824
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    References listed on IDEAS

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    1. Zinoviy Landsman & Emiliano Valdez, 2003. "Tail Conditional Expectations for Elliptical Distributions," North American Actuarial Journal, Taylor & Francis Journals, vol. 7(4), pages 55-71.
    2. 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.
    3. Yu Feng & Erik Schlogl, 2018. "Model Risk Measurement under Wasserstein Distance," Papers 1809.03641, arXiv.org, revised Mar 2019.
    4. Rüdiger Kiesel & Robin Rühlicke & Gerhard Stahl & Jinsong Zheng, 2016. "The Wasserstein Metric and Robustness in Risk Management," Risks, MDPI, vol. 4(3), pages 1-14, August.
    5. Jin Xisong & Lehnert Thorsten, 2018. "Large portfolio risk management and optimal portfolio allocation with dynamic elliptical copulas," Dependence Modeling, De Gruyter, vol. 6(1), pages 19-46, February.
    6. Lakshithe Wagalath & Jorge Zubelli, 2018. "A Liquidation Risk Adjustment For Value At Risk And Expected Shortfall," Post-Print hal-02572794, HAL.
    7. Lakshithe Wagalath & Jorge P. Zubelli, 2018. "A Liquidation Risk Adjustment For Value At Risk And Expected Shortfall," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 21(03), pages 1-21, May.
    8. Giovanni BARONE-ADESI & Kostas GIANNOPOULOS & Les VOSPER, 2015. "Estimating the Joint Tail Risk Under the Filtered Historical Simulation. An Application to the CCP's Default and Waterfall Fund," Swiss Finance Institute Research Paper Series 15-12, Swiss Finance Institute.
    9. Jianping Li & Jichuang Feng & Xiaolei Sun & Minglu Li, 2012. "Risk Integration Mechanisms And Approaches In Banking Industry," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 11(06), pages 1183-1213.
    10. 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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    Cited by:

    1. Christa Cuchiero & Guido Gazzani & Irene Klein, 2022. "Risk measures under model uncertainty: a Bayesian viewpoint," Papers 2204.07115, arXiv.org.

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