Bootstrap testing for detrended fluctuation analysis

Detrended fluctuation analysis (DFA) is a scaling method that allows the detection of long memory in a time series. Until now no asymptotic distribution has been found for this statistic. The bootstrap technique allows the simulation of the probability distribution of any statistic. In this paper the results of the Monte Carlo study using bootstrap method show that the DFA test has reasonably good power for short time series. Another advantage of the bootstrap technique is that allows the calculation of finite sample critical values. As an example we calculate bootstrap p-values for financial returns time series using DFA.