Hello All,

I am new to R, and I am writing to seek your advice on how best to use it to run
R's various normality tests in an automated way.

In a nutshell, my situation is as follows. I work in an investment bank, and my
team and I are concerned that the assumption we make in our models that the
returns of assets are normally distributed may not be justified for certain
asset classes. We are keen to check this statistically.

To this end, we have an Excel document which contains historical data on the
returns of the asset classes we want to investigate, and we would like to run
R's multiple normality tests on these data to check whether any asset classes
are flagged up as being statistically non-normal.

I see from the R documentation that there are several R commands to test for
this, but is it possible to progamme a tool which can (i) convert the Excel data
into a format which R can read, then (ii) run all the relevant tests from R,
then (iii) compare the results (such as the p-values) with a user-defined
benchmark, and (iv) output a file which shows for each asset class, which tests
reveal that the null hypothesis of normality is rejected?

My team and I would be very grateful for your advice on this.

Yours sincerely,

Alex.

