Ambiguity in portfolio selection

By Georg Ch. Pflug, David Wozabal. 2007

303 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/84f25420cbb177e860534d39ac8b206c6a871ff8

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Citations in the corpus

Distributionally Robust Optimization: A Review (2019, methodology): “Wozabal [229], Mehrotra and Zhang [203], Mohajerin Esfahani and Kuhn [205], Gao and Kleywegt [110], Chen et al.”
Learning Models with Uniform Performance via Distributionally Robust Optimization (2018, background): “An alternative to our f -divergence based sets {Q : Df (Q||P0) ≤ ρ} are Wasserstein balls [99, 72, 100, 81, 18, 17, 41, 38, 87, 65].”
Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach (2016, methodology): “e, to allow easier computation), as well as specifyinga desired conﬁdence level for appropriate robustness. The recent line of work on distributionally robust optimization using Wasserstein distances [53, 70, 28, 59, 12] is similar in spirit to the formulation considered here. Unlike f-divergences, ”
Data-driven risk-averse stochastic optimization with Wasserstein metric (2018, background): “Key words: stochastic optimization; data-driven decision making; Wasserstein metric ∗The basic idea and main results of this paper are also available in [29] and in the first author’s dissertation [27].”
Quantifying Distributional Model Risk via Optimal Transport (2016, unknown): “…et al. (2015); Jiang and Guan (2015); Hu and Hong (2012); Bayraksan and Love (2015) for KL-divergence and other likelihood based uncertainty sets, Pflug and Wozabal (2007); Wozabal (2012); Esfahani and Kuhn (2015); Zhao and Guan (2015); Gao and Kleywegt (2016) for Wasserstein distance based…”
Generalized Quantiles as Risk Measures (2014, background): “Pflug and Wozabal (2007) proved a similar result for CVaR, while a discussion of the same property for general coherent risk measures can be found in Pichler (2012).”
Ambiguity in asset pricing and portfolio choice: a review of the literature (2010, unknown): “When we take into account also the concern for model misspecification, the term+ is multiplied by an adjustment for model uncertainty:  + ≡ exp (− ∗(x+ ))  [exp (− ∗(x+ )) |x]  (159)”
The 1/ N investment strategy is optimal under high model ambiguity (2012, methodology): “See Maenhout (2004); Calafiore (2007); Pflug and Wozabal (2007); Garlappi et al. (2007); Quaranta and Zaffaroni (2008); Vrontos et al. (2008); Kerkhof et al. (2010); Lutgens and Schotman (2010); Tarashev (2010); Wozabal (2010) for recent advances in this direction.”
Robust distortion risk measures (2022, unknown): “The Wasserstein distance has been widely applied to model distributional uncertainty in financial contexts (see, e.g., Bartl et al., 2020, Chen & Xie, 2021, and Pflug & Wozabal, 2007), and as a distance from reference model (see e.g., Pesenti, 2022 and Pesenti & Jaimungal, 2023).”
Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning (2019, methodology): “Thus, aims to transform training data to decisions, it is in some precise sense optimal Distributionally robust optimization models with Wasserstein ambiguity sets were introduced [84].”