Mean-Covariance Robust Risk Measurement

Viet-Anh Nguyen, Soroosh Shafieezadeh Abadeh, Damir Filipović and Daniel Kuhn
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Viet-Anh Nguyen: Ecole Polytechnique Federale de Lausanne - MTEI
Soroosh Shafieezadeh Abadeh: Carnegie Mellon University - David A. Tepper School of Business
Damir Filipović: Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute
Daniel Kuhn: École polytechnique fédérale de Lausanne

No 21-93, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: We introduce a universal framework for mean-covariance robust risk measurement and portfolio optimization.We model uncertainty in terms of the Gelbrich distance on the mean-covariance space, along with prior structural information about the population distribution. Our approach is related to the theory of optimal transport and exhibits superior statistical and computational properties than existing models. We find that, for a large class of risk measures, mean-covariance robust portfolio optimization boils down to the Markowitz model, subject to a regularization term given in closed form. This includes the finance standards, value-at-risk and conditional value-at-risk, and can be solved highly efficiently.

Keywords: Robust optimization; risk measurement; optimal transport (search for similar items in EconPapers)
Pages: 44 pages
Date: 2021-12
New Economics Papers: this item is included in nep-ore and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2193

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