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Journal ArticleDOI

Triple-objective models for portfolio optimisation with symmetric and percentile risk measures

TLDR
In this article, the authors compared three different triple-criteria portfolio optimisation models and showed that the results obtained prove that the triple-objective portfolio optimization models with value-at-risk and conditional value at-risk could be used to shape the distribution of portfolio returns, and that the decision maker can assess the value of portfolio return, the risk level and number of assets chosen in the portfolio, and can decide how to invest in real life situation comparing with ideal (optimal) portfolio solutions.
Abstract
The purpose of this paper is to compare three different triple-criteria portfolio optimisation models. The first model is constructed with the use of percentile risk measure value-at-risk and solved by mixed integer programming. The second one is constructed with the use of percentile risk measure conditional value-at-risk and solved by linear programming. The third one is constructed with the use of a symmetric measure of risk - variance of return - as in the Markowitz portfolio and solved by quadratic programming. Cardinality constraints are formulated in all models that limit the number of assets selected in the portfolio. Computational experiments are conducted for triple-criteria portfolio stock exchange investments. The results obtained prove that the triple-objective portfolio optimisation models with value-at-risk and conditional value-at-risk could be used to shape the distribution of portfolio returns. The decision maker can assess the value of portfolio return, the risk level and number of assets chosen in the portfolio, and can decide how to invest in a real life situation comparing with ideal (optimal) portfolio solutions. The proposed scenario-based portfolio optimisation problems under uncertainty, formulated as a triple-objective linear, mixed integer or quadratic program are solved using commercially available software (AMPL/CPLEX) for mathematical programming.

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Citations
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Journal ArticleDOI

Optimal portfolios with downside risk

TL;DR: Markowitz optimal portfolio theory (Markowitz 1987), also known as the Mean-Variance theory, has had a tremendous impact and hundreds of papers are devoted to this topic as mentioned in this paper.
Journal ArticleDOI

Electricity Portfolio Optimization for Large Consumers: Iberian Electricity Market Case Study

TL;DR: In this paper, a mixed integer programming model is presented to characterize the electricity portfolio of large consumers, where the energy sources available for the portfolio characterization are the day-ahead spot market, forward contracts, and self-generation.
Book ChapterDOI

Selected Multi-Criteria Green Vehicle Routing Problems

TL;DR: In this article, the authors optimize multi-criteria formulation for green vehicle routing problems by mixed integer programming, which is used to solve the road freight transportation of a Spanish company of groceries.
Journal ArticleDOI

Mean-risk-skewness models for portfolio optimization based on uncertain measure

TL;DR: In this paper, skewness is considered to measure the asymmetry of portfolio returns and a mean-risk-skewness model for portfolio selection will be proposed in uncertain environment and a hybrid intelligent algorithm is designed.
Book ChapterDOI

Selected Multiple Criteria Supply Chain Optimization Problems

Bartosz Sawik
TL;DR: This chapter presents a review of selected multiple criteria problems used in supply chain optimization, focused on the problem of supply chain when a disruption happens and strategies to deal with the issue of disruptions in supply network and how to mitigate the impact of disruptions.