Selecting Optimal Portfolio on the Basis of Value at Risk

Peng, Hongmei

doi:10.1109/cso.2009.203

Cited by 1 publication

(3 citation statements)

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“…In other words, VaR is defined as sufficient capital to cover the potential losses of a portfolio over a given period. Formally, in the framework of uncertainty theory, Peng [18] introduced, in 2009 , the following definition:…”

Section: Figure 1 Normal and Inverse Normal Uncertainty Distributionmentioning

confidence: 99%

“…Because of the non-coherence of the VaR risk measure, Peng [18] has been introduced a coherent risk measure called Tail Value at risk "TVaR" as follows:…”

Section: Figure 1 Normal and Inverse Normal Uncertainty Distributionmentioning

confidence: 99%

“…Risk measures are an important pilar in portfolio theory, great attention is given by scholars to studying this field. Peng [18] introduced, for the first time, the concepts of Value at Risk and Tail Value at Risk to the framework of uncertainty theory. Yan [19] introduced Mean-VaR uncertain portfolio selection problem.…”

Section: Introductionmentioning

confidence: 99%

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Mean-TVaR Models for Diversified Multi-period Portfolio Optimization with Realistic Factors based on Uncertainty Theory

Belabbes,

Mostafa,

El Abidine

2023

Stat., optim. inf. comput.

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The focus of any portfolio optimization problem is to imitate the stock markets and propose the optimal solutions to dealing with diverse investor expectations. In this paper, we propose new multi-period portfolio optimization problems when security returns are uncertain variables, given by experts’ estimations, and take the Tail value at risk (TVaR) as a coherent risk measure of investment in the framework of uncertainty theory. Real- constraints, in which transaction costs, liquidity of securities, and portfolio diversification, are taken into account. Equivalent deterministic forms of mean–TVaR models are proposed under the assumption that returns and liquidity of the securities obey some types of uncertainty distributions. We adapted the Delphi method in order to evaluate the expected, the standard deviation and the turnover rates values of returns of the given securities. Finally, numerical examples are given to illustrate the effectiveness of the proposed models.

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Section: Figure 1 Normal and Inverse Normal Uncertainty Distributionmentioning

confidence: 99%

“…Because of the non-coherence of the VaR risk measure, Peng [18] has been introduced a coherent risk measure called Tail Value at risk "TVaR" as follows:…”

Section: Figure 1 Normal and Inverse Normal Uncertainty Distributionmentioning

confidence: 99%