Step-by-Step: Portfolio Risk in Stata and Excel

Portfolio Risk in Excel:

To build our concept of the portfolio risk, we shall calculate it first manually in EXCEL, then we shall replicate the results using matrix notations in Stata.

Consider the following set of returns for two assets, i.e asset A and B.
A             B
.249917 .819483
.739069 .821416
.895491 .276843
.902722 .001586
.078344 .714815
.429804 .027261
.239556 .736011

As we know, the portfolio standard deviation under the modern portfolio theory is calculated as =

Thus, we need standard deviation of the two assets, proportion of investment in each asset (weights), and the covariance term between the two assets.
In Excel, we can find the standard deviation by

`=STDEV(range)`

And the formula for covariance is

`=COVARIANCE.S(range1, range2)`

`Thus, standard deviation of asset A = 0.3387`
`         `standard deviation of asset A = 0.3713
Covariance between the two = -0.0683
And if we invest equally in both assets, then the weight of A = 0.5 and weight of B = 0.5

Portfolio SD =( (0.5^2*0.3387^2)+(0.5^2*0.3713^2)+(2*0.5*0.5*-0.0683))^0.5

=(0.02897)^0.5
=0.1702

` `
Portfolio Risk in Stata

Finding portfolio standard deviation under the Modern Portfolio theory using matrix algebra requires three matrices
1. Weights of the assets in portfolio, in row format = W
2. Variance-Covariance matrix of assets returns = S
3. Weights of the assets in portfolio, in column format = W'

Portfolio SD = W * S * W'

NOTE: In order to find the variance-covariance matrix, you can install varrets program from ssc with:
`ssc install mvportStep 1 : Copy the example data to stata`
You can do either through copying the data from the excel file and pasting it to the stata editor.
Alternatively, you can copy the following and past it in the stata command line, or download the data file from here.
`input a b`
`.249917 ``.819483`
`.739069 ``.821416`
`.895491 ``.276843`
`.902722 ``.001586`
`.078344 ``.714815`
`.429804 ``.027261`
`.239556 ``.736011`
`end`

Step 2: Make variance covariance matrix

`varrets a b`

`mat S = r(cov)`

Step 3: Make a weight matrix
Assuming that we assign equal weights, we define matrix W

`mat W = (0.5, 0.5)`

Step 4 : Multiply the weight and variance-covariance matrix

`mat VAR = W * S * W'`

`Step 5: Show variance of the portfolio`

`mat list VAR`