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portopt

Portfolios on constrained efficient frontier

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portopt has been partially removed and will no longer accept ConSet or varargin arguments. Use Portfolio instead to solve portfolio problems that are more than a long-only fully-invested portfolio. For information on the workflow when using Portfolio objects, see Portfolio Object Workflow. For more information on migrating portopt code to Portfolio, see portopt Migration to Portfolio Object.

Syntax

[PortRisk,PortReturn,PortWts] = portopt(ExpReturn,ExpCovariance)
[PortRisk,PortReturn,PortWts] = portopt(___,NumPorts,PortReturn)
portopt(___,NumPorts,PortReturn)

Description

example

[PortRisk,PortReturn,PortWts] = portopt(ExpReturn,ExpCovariance) sets up the most basic portfolio problem with weights greater than or equal to 0 that must sum to 1. All that is necessary to solve this problem is the mean and covariance of asset returns. By default, portopt returns 10 equally-spaced points on the efficient frontier.

portopt solves the "standard" mean-variance portfolio optimization problem for a long-only fully-invested investor with no additional constraints. Specifically, every portfolios on the efficient frontier has non-negative weights that sum to 1.

example

[PortRisk,PortReturn,PortWts] = portopt(___,NumPorts,PortReturn) specifies options using one or more optional arguments in addition to the input arguments in the previous syntax.

example

portopt(___,NumPorts,PortReturn) returns a plot of the efficient frontier if portopt is invoked with no output arguments.

Input Arguments

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expected (mean) return of each asset, specified as a 1-by-number of assets (NASSETS) vector.

Data Types: double

Covariance of the asset returns, specified as a NASSETS-by-NASSETS matrix.

Data Types: double

(Optional) Number of portfolios generated along the efficient frontier, specified as a scalar numeric. Returns are equally spaced between the maximum possible return and the minimum risk point. If NumPorts is empty (entered as []), portopt computes 10 equally spaced points. If you specify 1, portopt returns the minimum-risk portfolio.

Note

If not over-ridden by PortReturn, these portfolios are spaced evenly from the minimum to the maximum return on the efficient frontier. If NumPorts = 1, then the minimum-risk portfolio is computed (positive integer).

Data Types: double

(Optional) Target portfolio returns to be computed on the efficient frontier, specified as a number of portfolios (NPORTS-by-1 vector). If not entered or empty, NumPorts equally spaced returns between the minimum and maximum possible values are used.

Note

portopt requires that if you set PortReturn, NumPorts should be empty. If you specify PortReturn with a nonempty vector, PortReturn overrides NumPorts. If any returns in PortReturn fall outside the range of returns on the efficient frontier, portopt generates a warning and the efficient portfolios closest to the endpoints of the efficient frontier are computed.

Data Types: double

Output Arguments

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Standard deviation of each portfolio, returned as a NPORTS-by-1 vector.

PortWts is an NPORTS-by-NASSETS matrix of weights allocated to each asset. Each row represents a portfolio. The total of all weights in a portfolio is 1.

Expected return of each portfolio, returned as a NPORTS-by-1 vector.

Weights allocated to each asset, returned as a NPORTS-by-NASSETS matrix. Each row represents a portfolio. The total of all weights in a portfolio is 1.

Examples

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Open Live Script

Use portopt to connect 20 portfolios along the efficient frontier having evenly spaced returns. By default, choose among portfolios without short-selling and scale the value of the portfolio to 1. 

ExpReturn = [0.1 0.2 0.15];

ExpCovariance = [0.005   -0.010    0.004
                -0.010    0.040   -0.002
                 0.004   -0.002    0.023];

NumPorts = 20;
portopt(ExpReturn, ExpCovariance, NumPorts)

See Also

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Topics

Introduced before R2006a

Financial Toolbox Documentation

Support

A Practical Guide to Modeling Financial Risk with MATLAB

A Practical Guide to Modeling Financial Risk with MATLAB

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