Portfolio Set for Optimization Using PortfolioCVaR 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 $$X\subset R^n$$ 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

$$A_{I}x\le b_I$$

where:

x is the portfolio (n vector).

A_I is the linear inequality constraint matrix (n_I-by-n matrix).

b_I is the linear inequality constraint vector (n_I vector).

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

PortfolioCVaR object properties to specify linear inequality constraints are:

• AInequality for A_I

• bInequality for b_I

• 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

$$A_{E}x=b_E$$

where:

x is the portfolio (n vector).

A_E is the linear equality constraint matrix (n_E-by-n matrix).

b_E is the linear equality constraint vector (n_E vector).

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

PortfolioCVaR object properties to specify linear equality constraints are:

• AEquality for A_E

• bEquality for b_E

• 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

$$l_B\le x\le u_B$$

where:

x is the portfolio (n vector).

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

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

n is the number of assets in the universe.

PortfolioCVaR object properties to specify bound constraints are:

• LowerBound for l_B

• UpperBound for u_B

• NumAssets for n

The default is to ignore these constraints.

The default portfolio optimization problem (see Default Portfolio Problem) has l_B = 0 with u_B 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

$$l_S\le 1^{T}x\le u_S$$

where:

x is the portfolio (n vector).

1 is the vector of ones (n vector).

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

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

n is the number of assets in the universe.

PortfolioCVaR object properties to specify budget constraints are:

• LowerBudget for l_S

• UpperBudget for u_S

• NumAssets for n

The default is to ignore this constraint.

The default portfolio optimization problem (see Default Portfolio Problem) has l_S = u_S = 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 l_S = 0 and u_S = 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 Portfolio Problem Specification 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

$$l_G\le Gx\le u_G$$

where:

x is the portfolio (n vector).

l_G is the lower-bound group constraint (n_G vector).

u_G is the upper-bound group constraint (n_G vector).

G is the matrix of group membership indexes (n_G-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.

PortfolioCVaR object properties to specify group constraints are:

• GroupMatrix for G

• LowerGroup for l_G

• UpperGroup for u_G

• 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

$$l_{Ri}{(G_{B}x)}_i\le {(G_{A}x)}_i\le u_{Ri}{(G_{B}x)}_i$$

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

x is the portfolio (n vector).

l_R is the vector of lower-bound group ratio constraints (n_R vector).

u_R is the vector matrix of upper-bound group ratio constraints (n_R vector).

G_A is the matrix of base group membership indexes (n_R-by-n matrix).

G_B is the matrix of comparison group membership indexes (n_R-by-n matrix).

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

Each row of G_A and G_B 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.

PortfolioCVaR object properties to specify group ratio constraints are:

• GroupA for G_A

• GroupB for G_B

• LowerRatio for l_R

• UpperRatio for u_R

• 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™ computes portfolio turnover as the average of purchases and sales. Average turnover constraints take the form

$$\frac{1}{2}{1}^T|x-x_0|\le \tau$$

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.

PortfolioCVaR 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

$$1^T\times \max \left\{0,x-x_0\right\}\le {\tau }_B$$

$$1^T\times \max \left\{0,x_0-x\right\}\le {\tau }_S$$

where:

x is the portfolio (n vector)

1 is the vector of ones (n vector).

x₀ is the Initial portfolio (n vector).

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

τ_S 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 τ_B

• SellTurnover for τ_S

• InitPort for x₀

The default is to ignore this constraint.

Note

The average turnover constraint (see Average Turnover Constraints) with τ is not a combination of the one-way turnover constraints with τ = τ_B = τ_S.