A New Data-Driven Distributionally Robust Portfolio Optimization Method Based on Wasserstein Ambiguity Set

Du, Ningning; Liu, Yankui; Liu, Ying

doi:10.1109/access.2020.3047967

Cited by 17 publications

(16 citation statements)

References 40 publications

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“…However, in most DRO problems, data-driven setting is more valuable. For a given dataset Ξ N with N sample points, it is used to estimate the moment information μ, Σ by (24) or to estimate the nominal distribution P N by ( 50) or (51) and radius D by (56). Therefore, for a a data-driven problem, the out-of-sample performance or the convergence of the model are important.…”

Section: Approximation Algorithmmentioning

confidence: 99%

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Distributionally Robust Optimization: A review on theory and applications

Lin¹,

Fang²,

Gao³

2022

NACO

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<p style='text-indent:20px;'>In this paper, we survey the primary research on the theory and applications of distributionally robust optimization (DRO). We start with reviewing the modeling power and computational attractiveness of DRO approaches, induced by the ambiguity sets structure and tractable robust counterpart reformulations. Next, we summarize the efficient solution methods, out-of-sample performance guarantee, and convergence analysis. Then, we illustrate some applications of DRO in machine learning and operations research, and finally, we discuss the future research directions.</p>

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Section: Approximation Algorithmmentioning

confidence: 99%

“…The Wasserstein ambiguity is also adopted in the portfolio selection models. Blanchet et al [24] studied the distributionally robust mean-variance portfolio selection model with a Wasserstein ambiguity set, while Du et al [51] focused on the data-driven mean-CVaR portfolio selection problem.…”

Section: Portfolio Optimizationmentioning

confidence: 99%

Distributionally Robust Optimization: A review on theory and applications

Lin¹,

Fang²,

Gao³

2022

NACO

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“…Due to the integrality constraints on x and y, the SMISOCP problem (3,4) is nonconvex and NP-hard in general. Below we list three special cases of this optimization problem.…”

Section: B Smisocp Problem Definitionmentioning

confidence: 99%

“…• If p = q = 0, then no integrality constraints are imposed on x and y. In this case, the SMISOCP problem (3,4) is reduced to the two-stage SSOCP problem with recourse [20]. SSOCP is a convex optimization problem that can be solved in several methods in polynomial time (see [28]- [31]).…”

Section: )mentioning

confidence: 99%

“…C HALLENGES, restrictions, and affects such as uncertainty [1]- [3], integrality [4]- [7], and conicity [8]- [11] arise naturally in real-world applications. For example, the (deterministic) mixed-integer secondorder cone programming (DMISOCP) models pre-sented in [12] (see also [13]- [15]) have proved to be useful in dealing with a variety of applications that involve integrality and conicity.…”

Section: Introductionmentioning

confidence: 99%

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Applications of Stochastic Mixed-Integer Second-Order Cone Optimization

Alzalg

Alioui

2022

IEEE Access

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Second-order cone programming problems are a tractable subclass of convex optimization problems that can be solved using polynomial algorithms. In the last decade, stochastic second-order cone programming problems have been studied, and efficient algorithms for solving them have been developed. The mixed-integer version of these problems is a new class of interest to the optimization community and practitioners, in which certain variables are required to be integers. In this paper, we describe five applications that lead to stochastic mixedinteger second-order cone programming problems. Additionally, we present solution algorithms for solving stochastic mixed-integer second-order cone programming using cuts and relaxations by combining existing algorithms for stochastic second-order cone programming with extensions of mixed-integer second-order cone programming. The applications, which are the focus of this paper, include facility location, portfolio optimization, uncapacitated inventory, battery swapping stations, and berth allocation planning. Considering the fact that mixed-integer programs are usually known to be NP-hard, bringing applications to the surface can detect tractable special cases and inspire for further algorithmic improvements in the future. INDEX TERMSSecond-order cone programming, Mixed-integer programming, Stochastic programming, Applications, Algorithms ACRONYMS CVaR Conditional value-at-risk. DMISOCP Deterministic mixed-integer second-order cone programming. FEU 40-foot equivalent unit. FLP Facility location problem. SMBSOCP Stochastic mixed-binary second-order cone programming. SMILP Stochastic mixed-integer linear programming. SMISOCP Stochastic mixed-integer second-order cone programming. SSOCP Stochastic second-order cone programming. TEU 20-foot equivalent unit.

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Analysis of New Approaches Used in Portfolio Optimization: A Systematic Literature Review

Milhomem

Dantas

2021

Adaptation, Learning, and Optimization

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Paper aims: To do a comprehensive review of the exact and heuristic methods, software/programming languages, constraints, and types of analysis (technical and fundamental) used to solve the portfolio optimization problem. Originality:The paper presents a useful discussion on aspects of portfolio optimization, both for researchers and investors and for finance professionals.Research method: A systematic literature review was performed, and the articles were compiled according to pre-established criteria/filters. Main findings:A point of attention should be given to the input data of optimization models. Depending on the degree of the estimation error of these input parameters, the optimization results may be lower than the results of the 1/N trading strategy.Implications for theory and practice: Robust optimization, Fuzzy logic, and prediction are examples of techniques used to reduce estimation errors. At the end of the article are pointed out trends and some gaps for future work.

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A New Data-Driven Distributionally Robust Portfolio Optimization Method Based on Wasserstein Ambiguity Set

Cited by 17 publications

References 40 publications

Distributionally Robust Optimization: A review on theory and applications

Distributionally Robust Optimization: A review on theory and applications

Applications of Stochastic Mixed-Integer Second-Order Cone Optimization

Analysis of New Approaches Used in Portfolio Optimization: A Systematic Literature Review

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