scholarly journals A new approach for worst-case regret portfolio optimization problem

2019 ◽  
Vol 12 (4-5) ◽  
pp. 761-770
Author(s):  
Ying Ji ◽  
◽  
Shaojian Qu ◽  
Yeming Dai ◽  
Keyword(s):  
Portfolio Optimization ◽  
Optimization Problem ◽  
Worst Case ◽  
New Approach ◽  
Portfolio Optimization Problem

MULTI-ASSET WORST-CASE OPTIMAL PORTFOLIOS

2019 ◽  
Vol 22 (04) ◽  
pp. 1950019
Author(s):  
RALF KORN ◽  
ELISABETH LEOFF
Keyword(s):  
Portfolio Optimization ◽  
Optimization Problem ◽  
Risky Asset ◽  
Lagrangian Multiplier ◽  
Optimal Portfolios ◽  
Power Utility ◽  
Worst Case ◽  
Stock Investment ◽  
Portfolio Approach ◽  
Portfolio Optimization Problem

We generalize the worst-case portfolio approach of Korn & Wilmott (2002) to a multi-asset setting. The nonuniqueness of indifference strategies results in a much more complicated portfolio optimization problem as in the single risky asset framework. To determine the worst-case optimal portfolio processes we develop two new approaches, a Lagrangian multiplier approach in the log-utility case and a combined constrained HJB equation and indifference strategy approach for dealing with power-utility functions. Various examples illustrate remarkable effects and differences compared to the single risky asset setting, in particular the possibility for using some stocks for crash hedging and thereby allowing stock investment possibilities that are not present in the single-stock case.

The worst-case discounted regret portfolio optimization problem

2014 ◽  
Vol 239 ◽  
pp. 310-319 ◽  
Author(s):  
Ying Ji ◽  
Tienan Wang ◽  
Mark Goh ◽  
Yong Zhou ◽  
Bo Zou
Keyword(s):  
Portfolio Optimization ◽  
Optimization Problem ◽  
Worst Case ◽  
Portfolio Optimization Problem

scholarly journals Modeling and Optimizing the Multi-Objective Portfolio Optimization Problem with Trapezoidal Fuzzy Parameters

2021 ◽  
Vol 26 (2) ◽  
pp. 36
Author(s):  
Alejandro Estrada-Padilla ◽  
Daniela Lopez-Garcia ◽  
Claudia Gómez-Santillán ◽  
Héctor Joaquín Fraire-Huacuja ◽  
Laura Cruz-Reyes ◽  
...  
Keyword(s):  
Steady State ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Solution Process ◽  
Density Estimator ◽  
Nsga Ii ◽  
Spatial Spread ◽  
Multi Objective ◽  
Significant Difference ◽  
Portfolio Optimization Problem

A common issue in the Multi-Objective Portfolio Optimization Problem (MOPOP) is the presence of uncertainty that affects individual decisions, e.g., variations on resources or benefits of projects. Fuzzy numbers are successful in dealing with imprecise numerical quantities, and they found numerous applications in optimization. However, so far, they have not been used to tackle uncertainty in MOPOP. Hence, this work proposes to tackle MOPOP’s uncertainty with a new optimization model based on fuzzy trapezoidal parameters. Additionally, it proposes three novel steady-state algorithms as the model’s solution process. One approach integrates the Fuzzy Adaptive Multi-objective Evolutionary (FAME) methodology; the other two apply the Non-Dominated Genetic Algorithm (NSGA-II) methodology. One steady-state algorithm uses the Spatial Spread Deviation as a density estimator to improve the Pareto fronts’ distribution. This research work’s final contribution is developing a new defuzzification mapping that allows measuring algorithms’ performance using widely known metrics. The results show a significant difference in performance favoring the proposed steady-state algorithm based on the FAME methodology.

Sign in / Sign up

Export Citation Format

Share Document