Portfolio Set for Optimization Using PortfolioMAD Object

The final element for a complete specification of a portfolio optimization problem is the set of feasible portfolios, which is called a portfolio set. A portfolio set XRn is specified by construction as the intersection of sets formed by a collection of constraints on portfolio weights. A portfolio set necessarily and sufficiently must be a nonempty, closed, and bounded set.

When setting up your portfolio set, ensure that the portfolio set satisfies these conditions. The most basic or “default” portfolio set requires portfolio weights to be nonnegative (using the lower-bound constraint) and to sum to 1 (using the budget constraint). The most general portfolio set handled by the portfolio optimization tools can have any of these constraints:

  • Linear inequality constraints

  • Linear equality constraints

  • Bound constraints

  • Budget constraints

  • Group constraints

  • Group ratio constraints

  • Average turnover constraints

  • One-way turnover constraints

Linear Inequality Constraints

Linear inequality constraints are general linear constraints that model relationships among portfolio weights that satisfy a system of inequalities. Linear inequality constraints take the form

AIxbI

where:

x is the portfolio (n vector).

AI is the linear inequality constraint matrix (nI-by-n matrix).

bI is the linear inequality constraint vector (nI vector).

n is the number of assets in the universe and nI is the number of constraints.

Portfolio object properties to specify linear inequality constraints are:

  • AInequality for AI

  • bInequality for bI

  • NumAssets for n

The default is to ignore these constraints.

Linear Equality Constraints

Linear equality constraints are general linear constraints that model relationships among portfolio weights that satisfy a system of equalities. Linear equality constraints take the form

AEx=bE

where:

x is the portfolio (n vector).

AE is the linear equality constraint matrix (nE-by-n matrix).

bE is the linear equality constraint vector (nE vector).

n is the number of assets in the universe and nE is the number of constraints.

Portfolio object properties to specify linear equality constraints are:

  • AEquality for AE

  • bEquality for bE

  • NumAssets for n

The default is to ignore these constraints.

'Simple' Bound Constraints

'Simple' Bound constraints are specialized linear constraints that confine portfolio weights to fall either above or below specific bounds. Since every portfolio set must be bounded, it is often a good practice, albeit not necessary, to set explicit bounds for the portfolio problem. To obtain explicit bounds for a given portfolio set, use the estimateBounds function. Bound constraints take the form

lBxuB

where:

x is the portfolio (n vector).

lB is the lower-bound constraint (n vector).

uB is the upper-bound constraint (n vector).

n is the number of assets in the universe.

Portfolio object properties to specify bound constraints are:

  • LowerBound for lB

  • UpperBound for uB

  • NumAssets for n

The default is to ignore these constraints.

The default portfolio optimization problem (see Default Portfolio Problem) has lB = 0 with uB set implicitly through a budget constraint.

Budget Constraints

Budget constraints are specialized linear constraints that confine the sum of portfolio weights to fall either above or below specific bounds. The constraints take the form

lS1TxuS

where:

x is the portfolio (n vector).

1 is the vector of ones (n vector).

lS is the lower-bound budget constraint (scalar).

uS is the upper-bound budget constraint (scalar).

n is the number of assets in the universe.

Portfolio object properties to specify budget constraints are:

  • LowerBudget for lS

  • UpperBudget for uS

  • NumAssets for n

The default is to ignore this constraint.

The default portfolio optimization problem (see Default Portfolio Problem) has lS = uS = 1, which means that the portfolio weights sum to 1. If the portfolio optimization problem includes possible movements in and out of cash, the budget constraint specifies how far portfolios can go into cash. For example, if lS = 0 and uS = 1, then the portfolio can have 0–100% invested in cash. If cash is to be a portfolio choice, set RiskFreeRate (r0) to a suitable value (see Return Proxy and Working with a Riskless Asset).

Group Constraints

Group constraints are specialized linear constraints that enforce “membership” among groups of assets. The constraints take the form

lGGxuG

where:

x is the portfolio (n vector).

lG is the lower-bound group constraint (nG vector).

uG is the upper-bound group constraint (nG vector).

G is the matrix of group membership indexes (nG-by-n matrix).

Each row of G identifies which assets belong to a group associated with that row. Each row contains either 0s or 1s with 1 indicating that an asset is part of the group or 0 indicating that the asset is not part of the group.

Portfolio object properties to specify group constraints are:

  • GroupMatrix for G

  • LowerGroup for lG

  • UpperGroup for uG

  • NumAssets for n

The default is to ignore these constraints.

Group Ratio Constraints

Group ratio constraints are specialized linear constraints that enforce relationships among groups of assets. The constraints take the form

lRi(GBx)i(GAx)iuRi(GBx)i

for i = 1,..., nR where:

x is the portfolio (n vector).

lR is the vector of lower-bound group ratio constraints (nR vector).

uR is the vector matrix of upper-bound group ratio constraints (nR vector).

GA is the matrix of base group membership indexes (nR-by-n matrix).

GB is the matrix of comparison group membership indexes (nR-by-n matrix).

n is the number of assets in the universe and nR is the number of constraints.

Each row of GA and GB identifies which assets belong to a base and comparison group associated with that row.

Each row contains either 0s or 1s with 1 indicating that an asset is part of the group or 0 indicating that the asset is not part of the group.

Portfolio object properties to specify group ratio constraints are:

  • GroupA for GA

  • GroupB for GB

  • LowerRatio for lR

  • UpperRatio for uR

  • NumAssets for n

The default is to ignore these constraints.

Average Turnover Constraints

Turnover constraint is a linear absolute value constraint that ensures estimated optimal portfolios differ from an initial portfolio by no more than a specified amount. Although portfolio turnover is defined in many ways, the turnover constraints implemented in Financial Toolbox™ compute portfolio turnover as the average of purchases and sales. Average turnover constraints take the form

121T|xx0|τ

where:

x is the portfolio (n vector).

1 is the vector of ones (n vector).

x0 is the initial portfolio (n vector).

τ is the upper bound for turnover (scalar).

n is the number of assets in the universe.

Portfolio object properties to specify the average turnover constraint are:

  • Turnover for τ

  • InitPort for x0

  • NumAssets for n

The default is to ignore this constraint.

One-way Turnover Constraints

One-way turnover constraints ensure that estimated optimal portfolios differ from an initial portfolio by no more than specified amounts according to whether the differences are purchases or sales. The constraints take the forms

1T×max{0,xx0}τB

1T×max{0,x0x}τS

where:

x is the portfolio (n vector)

1 is the vector of ones (n vector).

x0 is the Initial portfolio (n vector).

τB is the upper bound for turnover constraint on purchases (scalar).

τS is the upper bound for turnover constraint on sales (scalar).

To specify one-way turnover constraints, use the following properties in the Portfolio, PortfolioCVaR, or PortfolioMAD object:

  • BuyTurnover for τB

  • SellTurnover for τS

  • InitPort for x0

The default is to ignore this constraint.

Note

The average turnover constraint (see Working with Average Turnover Constraints Using PortfolioMAD Object) with τ is not a combination of the one-way turnover constraints with τ = τB = τS.

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