  A major issue in financial economics is the behavior of asset returns over
long horizons. Various estimators of long range dependence have been proposed.
Even though some have known asymptotic properties, it is important to test
their accuracy by using simulated series of different lengths. We test R/S
analysis, Detrended Fluctuation Analysis and periodogram regression methods on
samples drawn from Gaussian white noise. The DFA statistics turns out to be the
unanimous winner. Unfortunately, no asymptotic distribution theory has been
derived for this statistics so far. We were able, however, to construct
empirical (i.e. approximate) confidence intervals for all three methods. The
obtained values differ largely from heuristic values proposed by some authors
for the R/S statistics and are very close to asymptotic values for the
periodogram regression method.
