  In this paper, we present the results of Monte Carlo simulations for two
popular techniques of long-range correlations detection - classical and
modified rescaled range analyses. A focus is put on an effect of different
distributional properties on an ability of the methods to efficiently
distinguish between short and long-term memory. To do so, we analyze the
behavior of the estimators for independent, short-range dependent, and
long-range dependent processes with innovations from 8 different distributions.
We find that apart from a combination of very high levels of kurtosis and
skewness, both estimators are quite robust to distributional properties.
Importantly, we show that R/S is biased upwards (yet not strongly) for
short-range dependent processes, while M-R/S is strongly biased downwards for
long-range dependent processes regardless of the distribution of innovations.
