TRACTABLE ROBUST EXPECTED UTILITY AND RISK MODELS FOR PORTFOLIO OPTIMIZATION

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Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning (2019, background): “Distributionally robust optimization problems akin to (20a) and (20b) that accommodate a second layer of robustness to account for moment ambiguity have been investigated in [29, 42, 47, 66, 91, 116], among others.”
Distributionally Robust Optimization and Its Tractable Approximations (2010, methodology): “W are tractable conic representable sets (see Ben-Tal and Nemirovski [ 3 ], Bertsimas and Sim [8]).”
Infrastructure Planning for Electric Vehicles with Battery Swapping (2013, unknown): “2012), and portfolio optimization (Natarajan et al. 2010) under uncertainty.”
Robust distortion risk measures (2022, unknown): “…the first two moments but without a Wasserstein constraint have been extensively studied; indicatively see Kaas and Goovaerts (1986), Lo (1987), Grundy (1991), Hürlimann (2005), and De Schepper and Heijnen (2010) for the VaR, Li et al. (2018) for the RVaR, and Natarajan et al. (2010) for the TVaR.”
Recent advances in robust optimization: An overview (2014, background): “Natarajan et al. (2010) consider the case where only the mean, covariance and support information of asset returns are known and the investor's utility is piecewise linear concave.”