I would personally go for a normal returns, because you do not make any assumptions about the data or returns.
When we use log returns we assume that prices are distributed log normally (which, usually is very far from the truth).
Moreover if you will investigate different distribution you will not use the log returns features like time additivity or approximate raw-log equality.
And if you think about T-Student Distribution this is worth considering:
Mathematically there’s a problem: when you assume a student-t
distribution (a standard choice) of log returns, then you are
automatically assuming that the expected value of any such stock in
one day is infinity! This is usually not what people expect about the
market, especially considering that there does not exist an infinite
amount of money (yet!). I guess it’s technically up for debate whether
this is an okay assumption but let me stipulate that it’s not what
people usually intend.
This happens even at small scale, so for daily returns, and it’s
because the moment generating function is undefined for student-t
distributions (the moment generating function’s value at 1 is the
expected return, in terms of money, when you use log returns). We
actually saw this problem occur at Riskmetrics, where of course we
didn’t see “infinity” show up as a risk number but we saw, every now
and then, ridiculously large numbers when we let people combine “log
returns” with “student-t distributions.” A solution to this is to use
percentage returns when you want to assume fat tails.