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estimateFrontierByReturn

Estimate optimal portfolios with targeted portfolio returns

Use the estimateFrontierByReturn function with a Portfolio, PortfolioCVaR, or PortfolioMAD object to estimate optimal portfolios with targeted portfolio returns.

For details on the respective workflows when using these different objects, see Portfolio Object Workflow, PortfolioCVaR Object Workflow, and PortfolioMAD Object Workflow.

Syntax

[pwgt,pbuy,psell] = estimateFrontierByReturn(obj,TargetReturn)

Description

example

[pwgt,pbuy,psell] = estimateFrontierByReturn(obj,TargetReturn) estimates optimal portfolios with targeted portfolio returns.

Examples

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To obtain efficient portfolios that have targeted portfolio returns, the estimateFrontierByReturn function accepts one or more target portfolio returns and obtains efficient portfolios with the specified returns. Assume you have a universe of four assets where you want to obtain efficient portfolios with target portfolio returns of 6%, 9%, and 12%.

m = [ 0.05; 0.1; 0.12; 0.18 ];
C = [ 0.0064 0.00408 0.00192 0; 
      0.00408 0.0289 0.0204 0.0119;
      0.00192 0.0204 0.0576 0.0336;
      0 0.0119 0.0336 0.1225 ];
 
p = Portfolio;
p = setAssetMoments(p, m, C);
p = setDefaultConstraints(p);
pwgt = estimateFrontierByReturn(p, [0.06, 0.09, 0.12]);

display(pwgt);
pwgt = 

    0.8772    0.5032    0.1293
    0.0434    0.2488    0.4541
    0.0416    0.0780    0.1143
    0.0378    0.1700    0.3022

To obtain efficient portfolios that have targeted portfolio returns, the estimateFrontierByReturn function accepts one or more target portfolio returns and obtains efficient portfolios with the specified returns. Assume you have a universe of four assets where you want to obtain efficient portfolios with target portfolio returns of 7%, 10%, and 13%.

m = [ 0.05; 0.1; 0.12; 0.18 ];
C = [ 0.0064 0.00408 0.00192 0; 
    0.00408 0.0289 0.0204 0.0119;
    0.00192 0.0204 0.0576 0.0336;
    0 0.0119 0.0336 0.1225 ];

rng(11);

p = PortfolioCVaR;
p = simulateNormalScenariosByMoments(p, m, C, 2000);
p = setDefaultConstraints(p);
p = setProbabilityLevel(p, 0.95);

pwgt = estimateFrontierByReturn(p, [0.07 0.10, 0.13]);

display(pwgt);
pwgt = 

    0.7371    0.3071         0
    0.1504    0.3919    0.4396
    0.0286    0.1011    0.1360
    0.0839    0.1999    0.4244

The function rng() is used to reset the random number generator to produce the documented results. It is not necessary to reset the random number generator to simulate scenarios.

To obtain efficient portfolios that have targeted portfolio returns, the estimateFrontierByReturn function accepts one or more target portfolio returns and obtains efficient portfolios with the specified returns. Assume you have a universe of four assets where you want to obtain efficient portfolios with target portfolio returns of 7%, 10%, and 13%.

m = [ 0.05; 0.1; 0.12; 0.18 ];
C = [ 0.0064 0.00408 0.00192 0; 
    0.00408 0.0289 0.0204 0.0119;
    0.00192 0.0204 0.0576 0.0336;
    0 0.0119 0.0336 0.1225 ];

rng(11);

p = PortfolioMAD;
p = simulateNormalScenariosByMoments(p, m, C, 2000);
p = setDefaultConstraints(p);

pwgt = estimateFrontierByReturn(p, [0.07 0.10, 0.13]);

display(pwgt);
pwgt = 

    0.7436    0.3147    0.0000
    0.1357    0.3835    0.4422
    0.0328    0.0939    0.1324
    0.0879    0.2079    0.4254

The function rng() is used to reset the random number generator to produce the documented results. It is not necessary to reset the random number generator to simulate scenarios.

Input Arguments

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Object for portfolio, specified using Portfolio, PortfolioCVaR, or PortfolioMAD object. For more information on creating a portfolio object, see

Target values for portfolio return, specified as a NumPorts vector.

Note

TargetReturn specifies target returns for portfolios on the efficient frontier. If any TargetReturn values are outside the range of returns for efficient portfolios, the TargetReturn is replaced with the minimum or maximum efficient portfolio return, depending upon whether the target return is below or above the range of efficient portfolio returns.

Data Types: double

Output Arguments

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Optimal portfolios on the efficient frontier with specified target returns from TargetReturn, returned as a NumAssets-by-NumPorts matrix. pwgt is returned for a Portfolio, PortfolioCVaR, or PortfolioMAD input object (obj).

Purchases relative to an initial portfolio for optimal portfolios on the efficient frontier, returned as NumAssets-by-NumPorts matrix.

Note

If no initial portfolio is specified in obj.InitPort, that value is assumed to be 0 such that pbuy = max(0, pwgt) and psell = max(0, -pwgt).

pbuy is returned for a Portfolio, PortfolioCVaR, or PortfolioMAD input object (obj).

Sales relative to an initial portfolio for optimal portfolios on the efficient frontier, returned as a NumAssets-by-NumPorts matrix.

Note

If no initial portfolio is specified in obj.InitPort, that value is assumed to be 0 such that pbuy = max(0, pwgt) and psell = max(0, -pwgt).

psell is returned for Portfolio, PortfolioCVaR, or PortfolioMAD input object (obj).

Tips

You can also use dot notation to estimate optimal portfolios with targeted portfolio returns.

[pwgt, pbuy, psell] = obj.estimateFrontierByReturn(TargetReturn);

Introduced in R2011a


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