Robust Portfolio Selection Problems

By Donald Goldfarb, Garud N. Iyengar. Mathematics of Operations Research, 2003

985 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/747706b15fef87fe1c9971b0d5e4e7faeb59eb52

How researchers see this paper

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

In Economics and Econometrics

1. Dimitris J. Bertsimas
2. Guanghui Lan
3. Aharon Ben-Tal
4. Arkadi Nemirovski
5. Paul Glasserman
6. Xingbo Xu
7. Hamed Rahimian
8. Sanjay Mehrotra

In Management Science and Operations Research

1. Dimitris J. Bertsimas
2. Arkadi Nemirovski
3. Aharon Ben-Tal
4. Hamed Rahimian
5. Sanjay Mehrotra
6. Aurélie C. Thiele
7. Constantine Caramanis
8. David B. Brown

In Ocean Engineering

1. Arkadi Nemirovski
2. Aurélie C. Thiele
3. Hamed Rahimian
4. Sanjay Mehrotra
5. Dimitris J. Bertsimas
6. Constantine Caramanis
7. Aharon Ben-Tal
8. Paul Glasserman

Citations in the corpus

• Robust Asset Allocation (2004, methodology): “Goldfarb and Iyengar consider ellipsoidal uncertainty sets [10].”
• Ambiguous chance constrained problems and robust optimization (2006, background): “This phenomenon is well documented in several different application areas [3] [27].”
• Robust Dynamic Programming (2005, result): “Degradation in performance due to estimation errors in parameters has also been observed in other contexts (Ben-Tal and Nemirovski [3], Goldfarb and Iyengar [14]).”
• Adjustable Robust Optimization Models for a Nonlinear Two-Period System (2008, background): “Note that ellipsoidal uncertainty sets commonly seen in robust modeling of financial problems [13] can be approximated by uniform sampling from the boundary of the ellipsoid and using the corresponding inscribed polytope.”
• Data-driven robust optimization (2013, methodology): “Portfolio management has been well-studied in the robust optimization literature [18, 28, 38].”
• Size Matters: Optimal Calibration of Shrinkage Estimators for Portfolio Selection (2011, background): “…models (MacKinlay and Pastor (2000), Pastor (2000), Pastor and Stambaugh (2000)), robust optimization methods (Cornuejols and Tutuncu (2007), Goldfarb and Iyengar (2003), Garlappi et al. (2007), Rustem et al. (2000), Tutuncu and Koenig (2004)), Bayesian robust optimization (Wang (2005)),…”
• Worst-Case Conditional Value-at-Risk with Application to Robust Portfolio Management (2009, background): “…and ellipsoidal uncertainty, together with mixture distribution uncertainty, are the most often used uncertainty structures in robust optimization formulations (see, for example, Costa and Paiva 2002, Goldfarb and Iyengar 2003, Halldórsson and Tütüncü 2003 and El Ghaoui, Oks and Oustry 2003).”
• Vast Portfolio Selection with Gross-exposure Constraints (2012, positive): “Goldfarb and Iyengar (2003) studied some robust portfolio optimization problems.”
• Robust risk measurement and model risk (2014, positive): “Robust optimization seeks to optimize against worst-case errors in problem data — see Ben-Tal, Margalit, and Nemirovski [6], Bertsimas and Pachamanova [8], and Goldfarb and Iyengar [17], for example.”
• Robust Solutions of Optimization Problems Affected by Uncertain Probabilities (2013, neutral): “Related research on robust optimal portfolio choice with uncertainty sets based on confidence sets include [16], [23], and [24] (for an overview, see [22]).”
• A robust optimization approach for the capacitated vehicle routing problem with demand uncertainty (2008, background): “Robust solutions have the potential to be efficient solutions in practice, since they tend not to be far from the optimal solution of the deterministic problem and significantly outperform the deterministic optimal solution in the worst case (Goldfarb and Iyengar, 2003; Bertsimas and Sim, 2004).”
• Robust twin support vector machine for pattern classification (2013, methodology): “In this paper, following the line of the research in [19,20,24,30], we proposed a new robust twin support vector machines for data with measurement errors (called R-TWSVM), which is represented as a second-order cone programming (SOCP) [35].”
• Distributionally Robust Optimization: A Review (2019, methodology): “, Goldfarb and Iyengar [122] and Delage and Ye [82].”
• An Interior-Point Method for a Class of Saddle-Point Problems (2003, background): “F (x)G− ∑ iG1 m log[AxAb]i , ∀x∈X , (18)”
• Ambiguity in portfolio selection (2007, methodology): “…Markowitz model, then these parameters, as the mean return, the volatility and the correlations are estimated from the given data material and the estimation error may be quantified by estimating the standard errors or by determining confidence sets, see for instance Goldfarb and Iyengar (2003).”