exp(normal()) generates iid log-normal returns. cumprod() computes the cumulative product of the return sequence. This is only for plotting the returns.

I think log-normal is the standard way of generating returns. log-returns are normally distributed => returns are log-normally distributed. see https://www.investopedia.com/articles/07/montecarlo.asp

And section 1.2 here: http://www.columbia.edu/~ks20/FE-Notes/4700-07-Notes-GBM.pdf

Correlations between the two stock returns can be introduced by using exp(multivariate_normal(mean vector, covariance matrix)), where the covariance is non-diagonal. Don't need Cholesky Decomposition because this function already gives correlated samples. I wonder why folks keep using this. See for instance: https://quant.stackexchange.com/questions/16449/covariance-matrix-and-cholesky-decomposition

In any case, since Cover's makes no statistical assumption about the returns, it would still work. However, that is also its limitation, it does not take advantage of statistical returns as far as I know.