Robust risk measurement and model risk

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Quantifying Distributional Model Risk via Optimal Transport (2016, unknown): “KL) divergence, despite not being a proper metric, has been the most popular choice for d, thanks to the tractability of (1) when dis chosen as relative entropy or other likelihood based metrics (see [14,36,23,4] for the use in robust performance analysis, and [26,32,41,38,33,9,55,34,29,8] and refer”
Learning Models with Uniform Performance via Distributionally Robust Optimization (2018, background): “There is also substantial work in characterizing worstcase sensitivity of risk measures to distributional misspecification [44, 9, 60, 36, 61, 43].”
Robust distortion risk measures (2022, unknown): “Worst-(best-) case values under probability distance constraints have been considered by Glasserman and Xu (2014) and Lam (2016), who use the Kullback–Leibler divergence, as well as by Blanchet and Murthy (2019), who utilize distances stemming from mass transportation.”