Value-at-Risk in Portfolio Optimization: Properties and Computational Approach ⁄

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A Guide to Sample Average Approximation (2015, methodology): “This smoothing technique has been widely used, particularly for minimizing risk measures such as Value-atRisk (VaR) and conditional Value-at-Risk (CVaR) [2, 16], as well as for handling chance constraints [23].”
Portfolio optimization in the presence of dependent financial returns with long memory: A copula based approach. (2013, background): “For more discussions on the use of CVaR in optimization problems, see Uryasev (2000), Rockafellar and Uryasev (2000, 2002), Pflug (2000), Krokhmal et al. (2002), Gaivoronski and Pflug (2005), and Quaranta and Zaffaroni (2008).”
Stable distributions in the Black–Litterman approach to asset allocation (2007, methodology): “For comparisons between CVaR and VaR see Gaivoronski and Pflug (2005). where Y is distributed according to a t-student with > 1 degree of freedom.”
Fat tails, VaR and subadditivity☆ (2013, positive): “…considered superior to VaR, because they are subadditive, they have not gained much traction in practice.2 Subadditivity of positive homogeneous risk measures guarantees their convexity, which facilitates the identification of optimal portfolios, see e.g. Pflug (2005); Stoyanov et al. (2007).”