  This paper illustrates a procedure for fitting financial data with
$\alpha$-stable distributions. After using all the available methods to
evaluate the distribution parameters, one can qualitatively select the best
estimate and run some goodness-of-fit tests on this estimate, in order to
quantitatively assess its quality. It turns out that, for the two investigated
data sets (MIB30 and DJIA from 2000 to present), an $\alpha$-stable fit of
log-returns is reasonably good.
