MVT

Optimize portfolio allocations for absolute and relative performance with this multi-goal optimization function. Maximize return while minimizing risk and tracking-error (Mean-variance-tracking-error)

Description

For investors concerned with both absolute and relative performance, the mean-variance tracking-error (MVT) objective function maximizes a portfolio’s expected return while minimizing both risk and tracking error. This function also allows for the robust specifications of linear and nonlinear constraints.

Syntax

The following describes the function signature for use in Microsoft Excel's formula bar.

=MVT(mu, sigma, rho, λ1, λ2, wBenchmark, wInitial, tc, lb, ub, constraints, nonlincons)

Input(s)

Argument
Description

mu

Required. Vector of expected returns.

sigma

Required. Vector of expected risk.

rho

Required. Correlation matrix.

λ1

Optional. Scalar value for risk aversion. If not specified, it defaults to 1.

λ2

Optional. Scalar value for tracking aversion. If not specified, it defaults to 1.

wBenchmark

Optional. Vector of benchmark weights. If not specified, the function assumes a vector of zeros.

wInitial

Optional. Vector of initial weights (or your current weights). If not specified, the function assumes a vector of zeros.

tc

Optional. Vector of transaction costs. If not specified, the function assumes a vector of zeros.

lb

Optional. Vector of lower bound limits. If not specified, it defaults to a vector of zeros.

ub

Optional. Vector of upper bound limits. If the argument is not specified, it defaults to a vector of ones.

constraints

nonlincons

Output(s)

The output matrix follows the vector orientation of mu (column / row). If you have specified your inputs as column-vectors, the corresponding output matrix will be transpose of the above.

exitFlag
Description

-2

No feasible solution found. Check your constraints and problem definition.

-1

Unexpected interruption.

0

Number of iterations exceeded.

1

First-order optimality measure is less than tolerance threshold and the constraints were satisfied.

2

Delta in optimal weights is less than the configured numerical step size.

3

Change in the expected utility value is less than the tolerance threshold.

4

Magnitude of search direction was less than the configured threshold.

5

Magnitude of directional derivative in the search direction was less than the configured threshold.

Example

Further Reading

Last updated