Optimizing portfolio selection problems under credibilistic CVaR criterion

Nai-qi, Liu; Chen, Yanju; Liu, Yankui

doi:10.3233/jifs-171298

Cited by 28 publications

(14 citation statements)

References 54 publications

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“…Qiu et al [30] obtained some algebraic proper ties and topological properties of the quotient space of fuzzy numbers with respect to the equivalence relation and introduced a new concept of convergence under which the quotient space was complete. At the same time, fuzzy theory integrated with optimization method has been applied successfully to more and more application-oriented fields [3,22,23]. The inclusion of fuzzy uncertainty in the context of sustainable development is a challenging issue in terms of modeling and solution.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing sustainable development problem under uncertainty: Robust vs fuzzy optimization methods

Jia

Bai

Song

et al. 2019

IFS

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Sustainable development problem involves several conflicting criteria on economy, energy, environment and social aspects and some sources of uncertainty due to its attribute. It is necessary to mitigate risks in sustainable development schedule. However, the existing literature includes limited quantitative research on robust strategies for sustainable development under uncertainty. Under these concerns, this paper presents a robust multi-objective optimization formulation for allocating labor across economic sectors to simultaneously satisfy economy (gross domestic product, GDP), environment (greenhouse gas (GHG) emissions), energy (electricity) and society (labor), in which the per capita GDP, per capita electricity consumption, per capita GHG and per capita rate of unemployment are robust uncertainty. There are three conflicting objectives in our sustainable development problem. The first objective maximizes the sectoral GDP, the second objective minimizes the sectoral electricity consumption and the third objective minimizes the sectoral GHG emissions. We adapt-constraint method to deal with the multiple objectives for the sustainable development problem, and obtain a more flexible result via an interactive decision-making process. Since uncertainty has often led to computationally intractable models, we reformulate the proposed model using robust optimization method into the tractable robust counterpart (RC) forms under two types of uncertainty sets. Finally, to demonstrate the effectiveness and applicability for our model, we conduct a case study for meeting year 2030 sustainable development of the United Arab Emirates (UAE). The numerical results show: (a) the robust multi-objective model is effective in uncertain environment and provides a reliable decision tool than deterministic model for integrated multi-objective sustainable development problem; (b) when the possibility distributions of uncertain parameters are available, fuzzy optimization model can provide better decision-making than robust model under box uncertainty set.

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Section: Literature Reviewmentioning

confidence: 99%

Optimizing sustainable development problem under uncertainty: Robust vs fuzzy optimization methods

Jia

Bai

Song

et al. 2019

IFS

Self Cite

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“…Proof. The fact is that ((26a) and (26b)) represents the RC of constraint (2), with the perturbation set being (25). Now let us prove that ((26a) and (26b)) is the safe approximation of constraint (5).…”

Section: The Safe Approximation Under Box + Ellipsoidal Perturbation mentioning

confidence: 99%

“…For example, Bai and Liu [24] introduced the second-order moment of fuzzy variables and constructed a class of mean-moment fuzzy portfolio optimization models. Liu et al [25] studied portfolio selection problem under credibilistic CVaR criterion. Bruni et al [26] proposed the exact and approximate stochastic dominance strategies for portfolio selection.…”

Section: Introductionmentioning

confidence: 99%

Robust Optimization Approximation for Ambiguous P-Model and Its Application

Jia

Bai

2018

Mathematical Problems in Engineering

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Robust optimization is a powerful and relatively novel methodology to cope with optimization problems in the presence of uncertainty. The positive aspect of robust optimization approach is its computational tractability that attracts more and more attention. In this paper, we focus on an ambiguous P-model where probability distributions are partially known. We discuss robust counterpart (RC) of uncertain linear constraints under two refined uncertain sets by robust approach and further find the safe tractable approximations of chance constraints in the ambiguous P-model. Because of the probability constraints embedded in the ambiguous P-model, it is computationally intractable. The advantage of our approach lies in choosing an implicit way to treat stochastic uncertainty models instead of solving them directly. The process above can enable the transformation of proposed P-model to a tractable deterministic one under the refined uncertainty sets. A numerical example about portfolio selection demonstrates that the ambiguous P-model can help the decision maker to determine the optimal investment proportions of various stocks. Sensitivity analyses explore the trade-off between optimization and robustness by adjusting parameter values. Comparison study is conducted to validate the benefit of our ambiguous P-model.

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“…Gupta et al [23] proposed a multi-criteria credibilistic portfolio rebalancing model considering portfolio risk as a risk curve. Liu et al [24] built a class of credibilistic mean-CVaR portfolio optimization models. Huang [25] provided two credibility-based portfolio selection models according to two types of chance criteria.…”

Section: Introductionmentioning

confidence: 99%

Credibilistic Mean-Semi-Entropy Model for Multi-Period Portfolio Selection with Background Risk

Zhang

2019

Entropy

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In financial markets, investors will face not only portfolio risk but also background risk. This paper proposes a credibilistic multi-objective mean-semi-entropy model with background risk for multi-period portfolio selection. In addition, realistic constraints such as liquidity, cardinality constraints, transaction costs, and buy-in thresholds are considered. For solving the proposed multi-objective problem efficiently, a novel hybrid algorithm named Hybrid Dragonfly Algorithm-Genetic Algorithm (HDA-GA) is designed by combining the advantages of the dragonfly algorithm (DA) and non-dominated sorting genetic algorithm II (NSGA II). Moreover, in the hybrid algorithm, parameter optimization, constraints handling, and external archive approaches are used to improve the ability of finding accurate approximations of Pareto optimal solutions with high diversity and coverage. Finally, we provide several empirical studies to show the validity of the proposed approaches.

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Optimizing portfolio selection problems under credibilistic CVaR criterion

Cited by 28 publications

References 54 publications

Optimizing sustainable development problem under uncertainty: Robust vs fuzzy optimization methods

Optimizing sustainable development problem under uncertainty: Robust vs fuzzy optimization methods

Robust Optimization Approximation for Ambiguous P-Model and Its Application

Credibilistic Mean-Semi-Entropy Model for Multi-Period Portfolio Selection with Background Risk

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