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Case study: Portfolio Optimization with Expectiles
Maximize Avg_g (maximizing the expected return of financial instruments)
subject to
expectile —————————————————————
| # of Variables | # of Scenarios | Objective Value | Solving Time, PC 3.14GHz (sec) | ||||
| Dataset 1 | 4 | 10000 | 0.00094986 | 0.06 | |||
|---|---|---|---|---|---|---|---|
| Environments | |||||||
| Run-File | Problem Statement | Data | Solution | ||||
| Matlab Toolbox | Data | ||||||
| Matlab | Matlab Code | Data | |||||
| R | R Code | Data | |||||
PROBLEM 2: minimizing expectile risk subject to bounded from below expected returns
Minimize expectile (minimize negative expectile risk of the portfolio)
subject to
Avg_g >= Const1 (constraint on the expected return of financial instruments)
Linear = Const2 (budget constraint)
Box constraints (box constraints for individual positions)
——————————————————————–
Avg_g = Average Gain
Box constraints = constraints on individual decision variables
——————————————————————–
| # of Variables | # of Scenarios | Objective Value | Solving Time, PC 3.14GHz (sec) | ||||
| Dataset 1 | 4 | 10000 | 0.02447960 | 0.01 | |||
|---|---|---|---|---|---|---|---|
| Environments | |||||||
| Run-File | Problem Statement | Data | Solution | ||||
| Matlab Toolbox | Data | ||||||
| Matlab | Matlab Code | Data | |||||
| R | R Code | Data | |||||
PROBLEM 3: maximizing the expected return subject to bounded negative expectile risk
Maximize Avg_g (maximizing the expected return of financial instruments)
subject to
expectile = Const1 (constraint on the expected return of financial instruments)
Linear = Const2 (budget constraint)
Box constraints (box constraints for individual positions)
——————————————————————–
Avg_g = Average Gain
Box constraints = constraints on individual decision variables
——————————————————————–
Case study: Portfolio Optimization with Expectiles Maximize Avg_g (maximizing the expected return of financial instruments) subject to expectile ————————————————————— # of Variables