Worst-Case Value-At-Risk and Robust Portfolio Optimization: A Conic Programming Approach

By Laurent El Ghaoui, Maksim Oks, François Oustry. Operational Research, 2003

759 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Numerical Analysis, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/172fc53432c0b1b8bd3d0da1be6f9363f27cfaa9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

In Numerical Analysis

In Statistics and Probability

Citations in the corpus

Distributionally Robust Stochastic Optimization with Wasserstein Distance (2016, background): “Yet another approach considers all distributions that are close, in the sense of statistical distance, to a nominal distribution ν, such as empirical distribution and Gaussian distribution ([18, 14]).”
Robust Portfolio Selection Problems (2003, background): “El Ghaoui et al. (2002) presents an alternative robust approach to VaR problems that results in semidefinite programs.”
On Distributionally Robust Chance-Constrained Linear Programs (2006, neutral): “5], the distributionally robust chance constraint (18) holds if √ (1/6) log (1/ )‖2P x̃‖ + φ̂(x) ≤ 0.”
The scenario approach to robust control design (2006, neutral): “folio optimization [29], and robust estimation [27].”
Robust risk measurement and model risk (2014, neutral): “The errors in problem data considered in this setting are generally limited to uncertainty about parameters, though distributional robustness is considered in, e.g. El Ghaoui et al. (2003) and Natarajan et al. (2008).”
Worst-Case Conditional Value-at-Risk with Application to Robust Portfolio Management (2009, background): “As an optimization problem, the right-hand side of (11) is equivalent to”
Robust portfolio selection under downside risk measures (2009, methodology): “We also recommend to the readers some alternative approaches for specifying the uncertainty structures of distributions given in Lobo and Boyd (2000), Goldfarb and Iyengar (2003), El Ghaoui et al. (2003), Erdogan and Iyengar (2006), Erdogan et al. (2006).”
Data-driven robust optimization (2013, methodology): “2 in El Ghaoui et al. (2003). See EC.”
Theory and Applications of Robust Optimization (2010, background): “One interesting extension in [57] is restricting the distributional family to be sufficiently “close” to some reference probability distribution P0.”
Portfolio optimization for heavy-tailed assets: Extreme Risk Index vs. Markowitz (2015, background): “Ghaoui et al. (2003) propose a worst-case Value-at-Risk and robustified programming approach based on only partial information about the return distributions, assuming that only bounds on the moments are known.”
Regret in the Newsvendor Model with Demand and Yield Randomness (2021, background): “One popular choice is the moment-based ambiguity sets that are mainly concerned with (generalized) moments (see e.g., Chen et al. 2019, Delage and Ye 2010, El Ghaoui et al. 2003, Goh and Sim 2010, Li et al. 2019, Wiesemann et al. 2014, Xie and Ahmed 2018).”
General Robust-Optimization Formulation for Nonlinear Programming (2007, methodology): “In particular, we refer to the works of Ben-Tal, Nemirovski and their co-authors [2]-[9], and the works of El Ghaoui and his co-authors [10]-[14].”
Robust possibilistic programming for socially responsible supply chain network design: A new approach (2012, neutral): “, [59]) in recent years.”
Evacuation Transportation Planning Under Uncertainty: A Robust Optimization Approach (2009, methodology): “The issue of robustness was relatively silent in the optimization community until the recent works of Ben-Tal and Nemirovski (1998, 1999, 2000), Ghaoui et al. (1997, 2003) and Bertsimas and Sim (2003, 2004).”
Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning (2019, background): “Convex uncertainty sets for ( µ, Σ) were shown render the outer maximization problems convex only in special cases, e.g. , when evaluating a worst-case value-at-risk of a linear or quadratic loss function [42, 91].”
Data-driven chance constrained stochastic program (2015, neutral): “[14]).”
Distributionally Robust Optimization: A Review (2019, methodology): “[101] study a distributionally robust one-period portfolio optimization, where the worst-case VaR over an ambiguity set of distributions with a known mean and covariance matrix is minimized.”
Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations (2015, methodology): “It has first been used in financial portfolio optimization to capture the distributional uncertainty of asset returns with a Gaussian nominal distribution [19].”
From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization (2010, background): “primitive uncertainties are characterized only by their means and covariance, the corresponding uncertainty set is an ellipsoid of the form U3. See, for instance, Bertsimas et al. [10] and El-Ghaoui et al. [ 17 ].”