Robust Optimization with Applications to Conditional Value-at-Risk-Based Portfolio Selection Problem

2012 ◽  
Vol 11 (1) ◽  
pp. 593-597
Author(s):  
Zhifeng Dai ◽  
Donghui Li ◽  
Fenghua Wen
Keyword(s):  
At Risk ◽  
Robust Optimization ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Selection Problem ◽  
Conditional Value At Risk ◽  
Portfolio Selection Problem

scholarly journals Multi-stage stochastic model in portfolio selection problem

Filomat ◽  
2018 ◽  
Vol 32 (3) ◽  
pp. 991-1001
Author(s):  
Shokoofeh Banihashemi ◽  
Ali Azarpour ◽  
Marziye Kaveh
Keyword(s):  
At Risk ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Selection Problem ◽  
Conditional Value At Risk ◽  
Expected Return ◽  
Portfolio Selection Problem ◽  
Multi Stage

This paper is a novel work of portfolio-selection problem solving using multi objective model considering four parameters, Expected return, downside beta coefficient, semivariance and conditional value at risk at a specified confidence level. Multi-period models can be defined as stochastic models. Early studies on portfolio selection developed using variance as a risk measure; although, theories and practices revealed that variance, considering its downsides, is not a desirable risk measure. To increase accuracy and overcoming negative aspects of variance, downside risk measures like semivarinace, downside beta covariance, value at risk and conditional value at risk was other risk measures that replaced in models. These risk measures all have advantages over variance and previous works using these parameters have shown improvements in the best portfolio selection. Purposed models are solved using genetic algorithm and for the topic completion, numerical example and plots to measure the performance of model in four dimensions are provided.

scholarly journals Mean-value at risk portfolio selection problem using clustering technique : A case study

2019 ◽  
Author(s):  
Sheshma Kiran Kumari ◽  
P. Kumar ◽  
J. Priya ◽  
S. Surya ◽  
A. K. Bhurjee
Keyword(s):  
At Risk ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Mean Value ◽  
Selection Problem ◽  
Portfolio Selection Problem ◽  
Clustering Technique

scholarly journals Formulation of the Non-Parametric Value at Risk Portfolio Selection Problem Considering Symmetry

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1639
Author(s):  
Dazhi Wang ◽  
Yanhua Chen ◽  
Hongfeng Wang ◽  
Min Huang
Keyword(s):  
At Risk ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Computation Time ◽  
Pso Algorithm ◽  
Selection Problem ◽  
Mixed Integer ◽  
Optimal Solutions ◽  
Portfolio Selection Problem ◽  
Non Parametric

In this research, we study the non-parametric portfolio selection problem with Value at Risk (VaR) minimization and establish a new enhanced Mixed Integer Linear Programming (MILP) formulation to obtain the optimal solutions considering the symmetric property of VaR. We identify that the new MILP formulation can significantly reduce the computation burden of the MILP solver CPLEX. To solve larger-scale practical portfolio selection problems in reasonable computation time, we also develop the Particle Swarm Optimization (PSO) algorithm integrating an efficient Fast Feasible Solution Detection (FFSD) scheme to obtain the near-optimal solutions. Using the simulated datasets with different distribution parameters and skewness and kurtosis patterns, some preliminary numerical results are provided to show the efficiency of the new formulation and FFSD scheme.

scholarly journals Robust Optimization-Based Commodity Portfolio Performance

2020 ◽  
Vol 8 (3) ◽  
pp. 54
Author(s):  
Ramesh Adhikari ◽  
Kyle J. Putnam ◽  
Humnath Panta
Keyword(s):  
At Risk ◽  
Robust Optimization ◽  
Value At Risk ◽  
Optimization Technique ◽  
Practical Importance ◽  
Optimization Techniques ◽  
Commodity Futures ◽  
Conditional Value At Risk ◽  
Holding Period ◽  
Mean Variance

This paper examines the performance of a naïve equally weighted buy-and-hold portfolio and optimization-based commodity futures portfolios for various lookback and holding periods using data from January 1986 to December 2018. The application of Monte Carlo simulation-based mean-variance and conditional value-at-risk optimization techniques are used to construct the robust commodity futures portfolios. This paper documents the benefits of applying a sophisticated, robust optimization technique to construct commodity futures portfolios. We find that a 12-month lookback period contains the most useful information in constructing optimization-based portfolios, and a 1-month holding period yields the highest returns among all the holding periods examined in the paper. We also find that an optimized conditional value-at-risk portfolio using a 12-month lookback period outperforms an optimized mean-variance portfolio using the same lookback period. Our findings highlight the advantages of using robust optimization for portfolio formation in the presence of return uncertainty in the commodity futures markets. The results also highlight the practical importance of choosing the appropriate lookback and holding period when using robust optimization in the commodity portfolio formation process.

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