Multivariate distributions are sets of random distributions that include some internal correlation. For example, if you are modeling various financial sectors, you might find that small cap equities are correlated with large cap equities; that each of these are correlated with international equities; and that all of these are correlated (perhaps negatively) with fixed income.

Multivariate distributions are useful in analyzing these kinds of models, because you can design distributions with internal correlations and then test them via simulation.

The current version of the RiskAMP Add-in includes a set of multivariate distributions. There are functions for modeling multivariate normal, lognormal, PERT, uniform, and triangular distributions.

All multivariate functions are prefaced with `Multivariate.`

. Multivariate functions are available for Normal, Log-normal, Uniform, Triangular, Beta and Beta-PERT distributions.

Older versions of the add-in had a different function for modeling the multivariate normal distribution — we’ve left that function in for compatibility, but we would recommend that new models be built using the new functions.

Constructing a distribution is straightforward, but it can be confusing because there are a number of specific requirements. Please read through this note and have a look at the example files to make sure you understand how it works.

You can try out the multivariate functions using the embedded spreadsheets on this page, and you can download each of them to try them in Excel (use the links on the right of each example).

Before constructing a distribution, you will need a correlation matrix describing the correlations among your several variates:

(These values were selected randomly and don’t reflect real-world correlation).

There are three requirements for the correlation matrix:

The matrix must have unit value (1.0) in the diagonal. Essentially this means that every value is 100% correlated with itself. (No other value would make sense).

The matrix must be symmetric; or, you can omit the upper-triangular, as in this example. We recommend using a matrix like this (omitting the upper triangular) as it reduces the chance of error.

The matrix must be positive-definite. The specifics of this are beyond the scope of this note; however if you generate your matrix from observed correlations, it will likely be positive-definite.

The **correlation matrix** tool on the RiskAMP toolbar can check if a matrix is positive-definite, and if not, make small adjustments.

For this example we will be using a multivariate normal distribution. Using other distributions is similar, except that other distributions may take different parameters.

When constructing a multivariate distribution, especially when you are getting started, we recommend using the **insert function** dialog box.

To construct a multivariate distribution, you must lay out the individual functions in a contiguous row or column. In this example I am using 4 cells in a column.

The first parameter to any multivariate distribution function is the set of individual distributions (the range containing the 4 cells). This is a little unusual, because each cell refers back to itself. Normally Excel would treat this as a circular reference, but here we use it to identify the full multivariate distribution.

This parameter is the same for all the distribution cells. Therefore we recommend that you use absolute (`$`

) references. That will also allow you to copy-and-paste the cell into the table.

In the example, this parameter is `$F$11:$F$14`

.

The second parameter refers to the correlation matrix. This should be the range containing the correlation matrix, and not including any header row or column; in the example, it is a 4x4 cell range. Again because this is the same for all the cells, we recommend using absolute (`$`

) references.

In the example, this parameter is `$C$4:$F$7`

.

Additional parameters to the multivariate distribution functions are different depending on the particular distribution. The normal distribution takes a mean and standard deviation.

In the example, these values are laid out in a table. This allows us to more easily make adjustments to the values, and it also allows us to copy and paste the distribution cells. Both of these help reduce the chance of errors in the functions.

For the first cell in the table, in row 12, the values are `C11`

and `D11`

. These refer back to the columns in the table. The full formula for the first row in the table, in `F11`

, is

```
=Multivariate.Normal($F$11:$F$14, $C$4:$F$7, C11, D11)
```

Because the first two parameters use absolute (`$`

) references, and the second two use relative references, we can just copy-and-paste the value of `F11`

into the rest of the table, `F12:F14`

.

You can verify correlations among the variates using the function `SimulationCorrelation`

. This function will show the correlation among any two cells during a simulation.

Try changing the correlation matrix (in cells `C4:F7`

), then run a simulation to see the validation (in `C18:F21`

). If you make a change to the correlation matrix and see **#ERR** in the multivariate distribution cells, that probably means the matrix is not positive-definite.

A correlated multivariate distribution can use different individual sub-distributions. Use the same techique as the example above, but change individual functions to your desired distribution.

This graph is connected to the above model and shows the cumulative distributions of the various random fields:

The last example is a model that uses separate multivariate distributions in multiple periods.

The parameters for the distribution are the same as in the first example; it uses the same correlation matrix and the same table of means and standard deviations.

Here, however, the multivariate distribution is laid out horizontally. Each row in the table contains a separate multivariate distribution representing returns in one period. The first parameter to the functions in each row is a reference to the four cells in that row.

This example also adds portfolio weights to calculate the aggregate return in each period. The example omits things like inflation and costs associated with rebalancing the portfolio in each period.