In this paper, we show how the sampling properties of the Hurst exponent
methods of estimation change with the presence of heavy tails. We run extensive
Monte Carlo simulations to find out how rescaled range analysis (R/S),
multifractal detrended fluctuation analysis (MF-DFA), detrending moving average
(DMA) and generalized Hurst exponent approach (GHE) estimate Hurst exponent on
independent series with different heavy tails. For this purpose, we generate
independent random series from stable distribution with stability exponent
{\alpha} changing from 1.1 (heaviest tails) to 2 (Gaussian normal distribution)
and we estimate the Hurst exponent using the different methods. R/S and GHE
prove to be robust to heavy tails in the underlying process. GHE provides the
lowest variance and bias in comparison to the other methods regardless the
presence of heavy tails in data and sample size. Utilizing this result, we
apply a novel approach of the intraday time-dependent Hurst exponent and we
estimate the Hurst exponent on high frequency data for each trading day
separately. We obtain Hurst exponents for S&P500 index for the period beginning
with year 1983 and ending by November 2009 and we discuss the surprising result
which uncovers how the market's behavior changed over this long period