Conditional value-at-risk: optimization algorithms and applications

By Stanislav Uryasev. IEEE Conference on Computational Intelligence for Financial Engineering & Economics, 2000

446 citations recorded by the source corpus.

Subfields: Finance, Management Science and Operations Research, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/f9c8f24948e9c05fc85633ba827e8b518859aa2a

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Citations in the corpus

Portfolio optimization with conditional value-at-risk objective and constraints (2001, methodology): “We, therefore, can swap the CVaR function and the expected return in the problem formulation (compared to (Rockafellar and Uryasev, 2000), thus minimizing the negative expected return with a CVaR constraint.”
Markowitz Revisited: Mean-Variance Models in Financial Portfolio Analysis (2001, background): “[3, 51, 66, 166, 200].”
Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming (2019, background): “As another powerful paradigm for optimization under uncertainty, chance constrained programming aims to optimize an objective while ensuring constraints to be satisfied with a specified probability in uncertain environment [62]{Uryasev, 2000 #2757}.”
Spectral measures of risk: A coherent representation of subjective risk aversion (2002, background): “It was recently discovered (Acerbi et al., 2001; Acerbi and Tasche, 2001, 2002; Rockafellar and Uryasev, 2000, 2001) that the a-expected shortfall ESðaÞ, correctly defined as the ‘‘average of the a100% worst losses’’ of a portfolio is a coherent risk measure 1 for any chosen confidence level a 2…”
Value-at-Risk vs. Conditional Value-at-Risk in Risk Management and Optimization (2008, methodology): “It has been proved, for example, in Acerbi and Tasche [2], Pflug [15], and Rockafellar and Uryasev [20], that for any probability level α∈]0,1[, R(X) = CVaRα(X) is a coherent measure of risk in the basic sense.”
Credit risk optimization with Conditional Value-at-Risk criterion (2001, methodology): “A simple description of the approach for minimization of CVaR and optimization problems with CVaR constraints can be found in [21].”
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).”
Canonical vine copulas in the context of modern portfolio management: Are they worth it? (2013, methodology): “Accordingly, we choose to minimize CVaR in preference to Value-at-Risk (VaR), as the former metric is considered to be a coherent risk measure (Uryasev, 2000).”
Expected Shortfall: a natural coherent alternative to Value at Risk (2001, background): “In this paper and henceforth we are going to take these axioms seriously as many other groups of researchers [4, 5, 6, 7, 9, 10], practitioners and regulators [11] have begun to do.”