estimatePortReturn

Estimate mean of portfolio returns

Syntax

pret = estimatePortReturn(obj,pwgt)

Description

pret = estimatePortReturn(obj,pwgt) estimates the mean of portfolio returns (as the proxy for portfolio return) for Portfolio, PortfolioCVaR, or PortfolioMAD objects. For details on the respective workflows when using these different objects, see Portfolio Object Workflow, PortfolioCVaR Object Workflow, and PortfolioMAD Object Workflow.

Examples

Estimate the Mean of Portfolio Returns for a Portfolio Object

Given portfolio p, use the estimatePortReturn function to estimate the mean of portfolio returns.

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 = estimateFrontierLimits(p);
pret = estimatePortReturn(p, pwgt);
disp(pret)

    0.0590
    0.1800

Estimate the Mean of Portfolio Returns for a PortfolioCVaR Object

Given portfolio p, use the estimatePortReturn function to estimate the mean of portfolio returns.

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 ];
m = m/12;
C = C/12;

rng(11);

AssetScenarios = mvnrnd(m, C, 20000);

p = PortfolioCVaR;
p = setScenarios(p, AssetScenarios);
p = setDefaultConstraints(p);
p = setProbabilityLevel(p, 0.95);

pwgt = estimateFrontierLimits(p);
pret = estimatePortReturn(p, pwgt);
disp(pret)

    0.0050
    0.0154

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

Estimate the Mean of Portfolio Returns for a PortfolioMAD Object

Given portfolio p, use the estimatePortReturn function to estimate the mean of portfolio returns.

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 ];
m = m/12;
C = C/12;

rng(11);

AssetScenarios = mvnrnd(m, C, 20000);

p = PortfolioMAD;
p = setScenarios(p, AssetScenarios);
p = setDefaultConstraints(p);

pwgt = estimateFrontierLimits(p);
pret = estimatePortReturn(p, pwgt);
disp(pret)

    0.0048
    0.0154

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

Input Arguments

obj — Object for portfolio

object

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

• Portfolio

• PortfolioCVaR

• PortfolioMAD

Data Types: object

pwgt — Collection of portfolios

matrix

Collection of portfolios, specified as a NumAssets-by-NumPorts matrix, where NumAssets is the number of assets in the universe and NumPorts is the number of portfolios in the collection of portfolios.

Data Types: double

Output Arguments

pret — Estimates for means of portfolio returns for each portfolio in pwgt

vector

Estimates for means of portfolio returns for each portfolio in pwgt, returned as a NumPorts vector.

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

Note

Depending on whether costs have been set, the portfolio return is either gross or net portfolio returns. For information on setting costs, see setCosts.

Tips

You can also use dot notation to estimate the mean of portfolio returns (as the proxy for portfolio return).

pret = obj.estimatePortReturn(pwgt);