Robust Asset Allocation

By Reha H. Tütüncü, Mark Koenig. Annals of Operations Research, 2004

419 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/e2425bae44567473073419379f49601a65755360

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. Melvyn Sim
2. Alberto Martín-Utrera
3. Dimitris J. Bertsimas
4. David B. Brown
5. Constantine Caramanis
6. Victor DeMiguel
7. Joline Uichanco
8. Georg Mainik

In Finance

1. Francisco Javier Nogales
2. Alberto Martín-Utrera
3. Victor DeMiguel
4. Dimitris J. Bertsimas
5. David B. Brown
6. Joline Uichanco
7. Melvyn Sim
8. Georg Mainik

In Management Science and Operations Research

1. Dimitris J. Bertsimas
2. Melvyn Sim
3. Constantine Caramanis
4. David B. Brown
5. Francisco Javier Nogales
6. Alberto Martín-Utrera
7. Victor DeMiguel
8. Jan-Joachim Rückmann

Citations in the corpus

• TRACTABLE ROBUST EXPECTED UTILITY AND RISK MODELS FOR PORTFOLIO OPTIMIZATION (2010, positive): “A related uncertainty set proposed by Tütüncü and Koenig (2004) assumes that the moments lie in a box region specified by upper and lower bounds.”
• Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach (2004, neutral): “Our paper differs from Goldfarb and Iyengar (2003), Halldórsson and Tütüncü (2000), and Tütüncü and Koenig (2004) in serval respects.”
• Generalized semi-infinite programming: A tutorial (2008, background): “Under similarly special assumptions a saddle point approach for robust programs is given in [95].”
• Portfolio optimization for heavy-tailed assets: Extreme Risk Index vs. Markowitz (2015, background): “Concerning robust asset allocation, Tütüncü and Koenig (2004) look for robust solutions that have the optimal worst-case performance, whereas Goldfarb and Iyengar (2003) choose worst-case estimators in a robust model framework that can be solved by linear programming.”
• Size Matters: Optimal Calibration of Shrinkage Estimators for Portfolio Selection (2011, methodology): “…(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)), robust estimation methods (DeMiguel and Nogales (2009)), and imposing…”
• Theory and Applications of Robust Optimization (2010, background): “• T¨ut¨unc¨u and Koenig [130] compute robust “efficient frontiers” using real-world market data.”