Portfolio class - Superclasses: AbstractPortfolio

Portfolio object for mean-variance portfolio optimization and analysis

Description

The portfolio object implements mean-variance portfolio optimization and is derived from the abstract portfolio optimization class AbstractPortfolio. Portfolio objects implement all methods in the AbstractPortfolio class along with methods that are specific to mean-variance portfolio optimization.

The main workflow for portfolio optimization is to create an instance of a portfolio object that completely specifies a portfolio optimization problem and to operate on the portfolio object to obtain and analyze efficient portfolios. A mean-variance optimization problem is completely specified with the following three elements:

A universe of assets with estimates for the prospective mean and covariance of asset total returns for a period of interest.
A portfolio set that specifies the set of portfolio choices in terms of a collection of constraints.
A model for portfolio return and risk, which, for mean-variance optimization, is either the gross or net mean of portfolio returns and the standard deviation of portfolio returns.

After you specify three elements in an unambiguous way, you can solve and analyze portfolio optimization problems. The simplest mean-variance portfolio optimization problem has:

A mean and covariance of asset total returns
Nonnegative weights for all portfolios that sum to 1 (the summation constraint is known as a budget constraint)
Built-in models for portfolio return and risk that use the mean and covariance of asset total returns

Given mean and covariance of asset returns in the variables AssetMean and AssetCovar, this problem is completely specified by:

p = Portfolio('AssetMean', AssetMean, 'AssetCovar', AssetCovar,...
'LowerBound', 0, 'Budget')

or equivalently by:

p = Portfolio;
p = p.setAssetMoments(AssetMean, AssetCovar); 
p = p.setDefaultConstraints;

Construction

p = Portfolio constructs an empty portfolio object for mean-variance portfolio optimization and analysis. You can then add elements to the portfolio object using Add Methods and Set Methods.

p = Portfolio(Name,Value) constructs a portfolio object for mean-variance portfolio optimization and analysis with additional options specified by one or more Name,Value pair arguments. Name is a property name and Value is its corresponding value. Name must appear inside single quotes (''). You can specify several name-value pair arguments in any order as Name1,Value1,…,NameN,ValueN.

p = Portfolio(p, Name,Value) constructs a portfolio object for mean-variance portfolio optimization and analysis using a previously constructed portfolio object p with additional options specified by one or more Name,Value pair arguments.

Input Arguments

`p`	(Optional) Previously constructed portfolio object (`p`).

Property Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments, where Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

`'AInequality'`	Linear inequality constraint matrix (`[]` or `[`matrix`]`). Default: `[]`
`'AssetCovar'`	Covariance of asset returns (`[]` or square matrix. Default: `[]`
`'AssetList'`	Names or symbols of assets in universe (`[]` or `[`vector cell of strings`]`). Default: `[]`
`'AssetMean'`	Mean of asset returns (`[]` or vector). Default: `[]`
`'bInequality'`	Linear inequality constraint vector (`[]` or `[`vector`]`). Default: `[]`
`'BuyCost'`	Proportional purchase costs (`[]` or vector). Default: `[]`
`'BuyTurnover'`	Turnover constraint on purchases (`[]` or `[`scalar`]`). Default: `[]`
`'GroupA'`	Group A weights to be bounded by weights in group B (`[]` or `[`matrix`]`). Default: `[]`
`'GroupB'`	Group B weights (`[]` or `[`matrix`]`). Default: `[]`
`'GroupMatrix'`	Group membership matrix (`[]` or `[`matrix`]`). Default: `[]`
`'InitPort'`	Initial portfolio (`[]` or vector). Default: `[]`
`'LowerBudget'`	Lower-bound budget constraint (`[]` or `[`scalar]). Default: `[]`
`'LowerGroup'`	Lower-bound group constraint (`[]` or `[`vector`]`). Default: `[]`
`'LowerRatio'`	Mnimum ratio of allocations between groups A and B (`[]` or `[`vector`]`). Default: `[]`
`'Name'`	Name for instance of the portfolio object (`[]` or `[`string`]`). Default: `[]`
`'NumAssets'`	Number of assets in universe (`[]` or `[`integer scalar`]`). Default: `[]`
`'RiskFreeRate'`	Risk-free rate (`[]` or scalar). Default: `[]`
`'SellCost'`	Proportional sales costs (`[]` or vector). Default: `[]`
`'SellTurnover'`	Turnover constraint on sales (`[]` or `[`scalar`]`). Default: `[]`
`'Turnover'`	Turnover constraint (`[]` or `[`scalar`]`). Default: `[]`
`'UpperBound'`	Upper-bound constraint (`[]` or `[`vector`]`). Default: `[]`
`'UpperBudget'`	Upper-bound budget constraint (`[]` or `[`scalar`]`). Default: `[]`
`'UpperGroup'`	Upper-bound group constraint (`[]` or `[`vector`]`). Default: `[]`
`'UpperRatio'`	Maximum ratio of allocations between groups A and B (`[]` or `[`vector`]`). Default: `[]`

Properties

The following properties are from the Portfolio class.

AssetCovar

Covariance of asset returns ([] or matrix).

Attributes: