Sometimes, you may want to validate either your inputs to, or outputs from, a portfolio
optimization problem. Although most error checking that occurs during the problem setup
phase catches most difficulties with a portfolio optimization problem, the processes to
validate portfolio sets and portfolios are time consuming and are best done offline. So,
the portfolio optimization tools have specialized functions to validate portfolio sets
and portfolios. For information on the workflow when using `Portfolio`

objects, see Portfolio Object Workflow.

Since it is necessary and sufficient that your portfolio set
must be a nonempty, closed, and bounded set to have a valid portfolio
optimization problem, the `estimateBounds`

function
lets you examine your portfolio set to determine if it is nonempty
and, if nonempty, whether it is bounded. Suppose that you have the
following portfolio set which is an empty set because the initial
portfolio at `0`

is too far from a portfolio that
satisfies the budget and turnover constraint:

p = Portfolio('NumAssets', 3, 'Budget', 1); p = setTurnover(p, 0.3, 0);

If a portfolio set is empty, `estimateBounds`

returns `NaN`

bounds
and sets the `isbounded`

flag to `[]`

:

[lb, ub, isbounded] = estimateBounds(p)

lb = NaN NaN NaN ub = NaN NaN NaN isbounded = []

Suppose that you create an unbounded portfolio set as follows:

p = Portfolio('AInequality', [1 -1; 1 1 ], 'bInequality', 0); [lb, ub, isbounded] = estimateBounds(p)

lb = -Inf -Inf ub = 1.0e-08 * -0.3712 Inf isbounded = logical 0

`estimateBounds`

returns (possibly
infinite) bounds and sets the `isbounded`

flag to
`false`

. The result shows which assets are unbounded so that
you can apply bound constraints as necessary.Finally, suppose that you created a portfolio set that is both nonempty and bounded. `estimateBounds`

not only validates
the set, but also obtains tighter bounds which are useful if you are concerned with
the actual range of portfolio choices for individual assets in your portfolio
set:

p = Portfolio; p = setBudget(p, 1,1); p = setBounds(p, [ -0.1; 0.2; 0.3; 0.2 ], [ 0.5; 0.3; 0.9; 0.8 ]); [lb, ub, isbounded] = estimateBounds(p)

lb = -0.1000 0.2000 0.3000 0.2000 ub = 0.3000 0.3000 0.7000 0.6000 isbounded = logical 1

In this example, all but the second asset has tighter upper bounds than the input upper bound implies.

Given a portfolio set specified in a `Portfolio`

object, you often want to
check if specific portfolios are feasible with respect to the portfolio set. This
can occur with, for example, initial portfolios and with portfolios obtained from
other procedures. The `checkFeasibility`

function
determines whether a collection of portfolios is feasible. Suppose that you perform
the following portfolio optimization and want to determine if the resultant
efficient portfolios are feasible relative to a modified problem.

First, set up a problem in the `Portfolio`

object `p`

,
estimate efficient portfolios in `pwgt`

, and then confirm that
these portfolios are feasible relative to the initial problem:

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 = estimateFrontier(p); checkFeasibility(p, pwgt)

ans = 1 1 1 1 1 1 1 1 1 1

Next, set up a different portfolio problem that starts with the initial problem with an additional a turnover constraint and an equally weighted initial portfolio:

q = setTurnover(p, 0.3, 0.25); checkFeasibility(q, pwgt)

ans = 0 0 0 1 1 0 0 0 0 0

`q`

.
Solving the second problem using `checkFeasibility`

demonstrates that
the efficient portfolio for `Portfolio`

object `q`

is feasible relative to the initial problem:qwgt = estimateFrontier(q); checkFeasibility(p, qwgt)

ans = 1 1 1 1 1 1 1 1 1 1

`Portfolio`

| `checkFeasibility`

| `estimateBounds`

- Creating the Portfolio Object
- Working with Portfolio Constraints Using Defaults
- Estimate Efficient Portfolios for Entire Efficient Frontier for Portfolio Object
- Estimate Efficient Frontiers for Portfolio Object
- Asset Allocation Case Study
- Portfolio Optimization Examples
- Portfolio Optimization with Semicontinuous and Cardinality Constraints
- Black-Litterman Portfolio Optimization
- Portfolio Optimization Using Factor Models