On robust portfolio and naïve diversification: mixing ambiguous and unambiguous assets

Paç, A. Burak; Pınar, Mustafa Ç.

doi:10.1007/s10479-017-2619-8

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…However, Pflug et al (2012) assumed that all assets are subject to uncertainty though it is possible to use fixed-income assets with no ambiguity or uncertainty in the portfolio. Therefore, Paç & Pınar (2018) extended the robust uniform strategy of Pflug et al (2012) by considering both ambiguous and unambiguous assets. They showed that by increasing the ambiguity level, measured by the radius of the ambiguity set, the optimal portfolio tends to use equal weights for all assets.…”

Section: Worst-case Var and Cvarmentioning

confidence: 99%

“…The inherent uncertainty about future asset returns, the abundance of public data available and the risk-averse nature of most investors make robust optimization an appealing approach in this area. As shown in this review paper, a wide range of robust PSP variants was studied, from a "plain vanilla" single-period, mean-variance PSP with a simple box uncertainty set (e.g., Tütüncü & Koenig (2004)) to formulations that consider advanced risk measures (e.g., , Huang et al (2010)), adaptive uncertainty sets (e.g., Yu (2016)), reallife investment strategies (e.g., Pflug et al (2012), Paç & Pınar (2018)) and dynamic portfolio balancing (e.g., Ling et al (2019), Cong & Oosterlee (2017)). This variety of modeling assumptions and approaches and the overlaps among them make it difficult to develop a unifying framework for robust PSPs, yet we adopted a multi-dimensional classification scheme that depends on the risk measure to be optimized, the type of uncertain parameters, the approach used to capture uncertainty and the the planning horizon (i.e., single-vs. multi-period).…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

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Robust Portfolio Selection Problems: A Comprehensive Review

Ghahtarani¹,

Ahmed²,

Ghasemi³

2021

Preprint

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In this paper, we provide a comprehensive review of recent advances in robust portfolio selection problems and their extensions, from both operational research and financial perspectives. A multi-dimensional classification of the models and methods proposed in the literature is presented, based on the types of financial problems, uncertainty sets, robust optimization approaches, and mathematical formulations. Several open questions and potential future research directions are identified.

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Section: Worst-case Var and Cvarmentioning

confidence: 99%

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Robust Portfolio Selection Problems: A Comprehensive Review

Ghahtarani¹,

Ahmed²,

Ghasemi³

2021

Preprint

View full text Add to dashboard Cite

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“…There are several deterministic methods available in the literature for solving similar minimax optimization problems (Yaman et al., 2007; Pınar and Paç, 2014; Pınar, 2016; Paç and Pınar, 2018). In this work, a different path was taken.…”

Section: Computing Relative‐robust and Absolute‐robust Solutionsmentioning

confidence: 99%

Portfolio selection under uncertainty: a new methodology for computing relative‐robust solutions

Caçador

Dias

Godinho

2019

Int Trans Operational Res

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In this paper, a new methodology for computing relative-robust portfolios based on minimax regret is proposed. Regret is defined as the utility loss for the investor resulting from choosing a given portfolio instead of choosing the optimal portfolio of the realized scenario. The absolute-robust strategy was also considered and, in this case, the minimum investor's expected utility in the worst-case scenario is maximized. Several subsamples are gathered from the in-sample data and for each subsample a minimax regret and a maximin solution are computed, to avoid the risk of overfitting. Robust portfolios are computed using a genetic algorithm, allowing the transformation of a three-level optimization problem in a two-level problem. Results show that the proposed relative-robust portfolio generally outperforms (other) relative-robust and non-robust portfolios, except for the global minimum variance portfolio. Furthermore, the relative-robust portfolio generally outperforms the absolute-robust portfolio, even considering higher risk aversion levels.

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A survey of decision making and optimization under uncertainty

Keith

Ahner

2019

Ann Oper Res

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On robust portfolio and naïve diversification: mixing ambiguous and unambiguous assets

Cited by 9 publications

References 29 publications

Robust Portfolio Selection Problems: A Comprehensive Review

Robust Portfolio Selection Problems: A Comprehensive Review

Portfolio selection under uncertainty: a new methodology for computing relative‐robust solutions

A survey of decision making and optimization under uncertainty

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