The Optimal Portfolio Model Based on Mean-CVaR

Yu, Xing; Sun, Hongguo; Chen, Guohua

doi:10.4236/jmf.2011.13017

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…The problem involves multi-objective optimization, and two optimization targets are considered in the proposed model. Although some algorithms, such as the Pareto solution set method [24] and the lexicographic method [25], are efficient for solving multi-objective optimization, the linear weighting-sum method [26] is used here for its better practicability and convenience. The two optimization goals are first normalized to the same range and then processed into a single objective function.…”

Section: Objective Functionmentioning

confidence: 99%

Spatial and Temporal Optimization Strategy for Plug-In Electric Vehicle Charging to Mitigate Impacts on Distribution Network

et al. 2018

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The large deployment of plug-in electric vehicles (PEVs) challenges the operation of the distribution network. Uncoordinated charging of PEVs will cause a heavy load burden at rush hour and lead to increased power loss and voltage fluctuation. To overcome these problems, a novel coordinated charging strategy which considers the moving characteristics of PEVs is proposed in this paper. Firstly, the concept of trip chain is introduced to analyze the spatial and temporal distribution of PEVs. Then, a stochastic optimization model for PEV charging is established to minimize the distribution network power loss (DNPL) and maximal voltage deviation (MVD). After that, the particle swarm optimization (PSO) algorithm with an embedded power flow program is adopted to solve the model, due to its simplicity and practicality. Last, the feasibility and efficiency of the proposed strategy is tested on the IEEE 33 distribution system. Simulation results show that the proposed charging strategy not only reduces power loss and the peak valley difference, but also improves voltage profile greatly.

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Section: Objective Functionmentioning

confidence: 99%

Spatial and Temporal Optimization Strategy for Plug-In Electric Vehicle Charging to Mitigate Impacts on Distribution Network

et al. 2018

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“…where the belief degree of set is equal to 0.95. In addition, in CVaR formula after linearization [16,17] we havẽ…”

Section: Mvc Model Of Portfolio Based On Lwsmmentioning

confidence: 99%

“…and ( , ) is the loss function which can be defined [16] by ( , ) = − . We can rewrite (19) according to the assumptions above as follows:…”

Section: Mvc Model Of Portfolio Based On Lwsmmentioning

confidence: 99%

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Mean-Variance-CvaR Model of Multiportfolio Optimization via Linear Weighted Sum Method

Elahi

Aziz

2014

Mathematical Problems in Engineering

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We propose a new approach to optimizing portfolios to mean-variance-CVaR (MVC) model. Although of several researches have studied the optimal MVC model of portfolio, the linear weighted sum method (LWSM) was not implemented in the area. The aim of this paper is to investigate the optimal portfolio model based on MVC via LWSM. With this method, the solution of the MVC model of portfolio as the multiobjective problem is presented. In data analysis section, this approach in investing on two assets is investigated. An MVC model of the multiportfolio was implemented in MATLAB and tested on the presented problem. It is shown that, by using three objective functions, it helps the investors to manage their portfolio better and thereby minimize the risk and maximize the return of the portfolio. The main goal of this study is to modify the current models and simplify it by using LWSM to obtain better results.

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“…While considering the risk of extreme loss, VaR and Conditional Value-at-Risk (CVaR), etc. are widely used, see, Guo and Li (2009) [10], Xu et al (2016) [11], Banihashemi (2017) [12], Yu et al (2011) [13] and the references therein. In particular, CVaR has attracted more attention since it has good theoretical properties, which is consistent with financial practice, see Artzner et al (1999) [14].…”

Section: Introductionmentioning

confidence: 99%

Optimal Asset Allocation for a Mean-Variance-CVaR Insurer under Regulatory Constraints

Yu¹,

Zhao²,

Yan³

2019

AJIBM

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In this paper, we introduce the mean-variance-CVaR criteria into the study of asset allocation for insurers. Considering that the financial market consists of one risk-free asset and multiple risky assets with regulatory constraints, an optimization problem is established for an insurer with underwriting business. Based on practical financial and insurance data, an empirical study is carried out. The results show that the mean-variance-CVaR model is able to provide more potential investment strategies for an insurer. The regulatory policy released by China Insurance Regulatory Commission plays a key role in controlling investment risk for Chinese insurers.

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The Optimal Portfolio Model Based on Mean-CVaR

Cited by 5 publications

References 11 publications

Spatial and Temporal Optimization Strategy for Plug-In Electric Vehicle Charging to Mitigate Impacts on Distribution Network

Spatial and Temporal Optimization Strategy for Plug-In Electric Vehicle Charging to Mitigate Impacts on Distribution Network

Mean-Variance-CvaR Model of Multiportfolio Optimization via Linear Weighted Sum Method

Optimal Asset Allocation for a Mean-Variance-CVaR Insurer under Regulatory Constraints

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