  Detrended fluctuation analysis (DFA) [1] of the volatility series has been
found to be useful in dentifying possible nonlinear/multifractal dynamics in
the empirical sample [2-4]. Long-range volatile correlation can be an outcome
of static as well as dynamical nonlinearity. In order to argue in favor of
dynamical nonlinearity, surrogate testing is used in conjunction with
volatility analysis [2-4]. In this brief communication, surrogate testing of
volatility series from long-range correlated noise and their static, invertible
nonlinear transforms is investigated. Long-range correlated monofractal noise
is generated using FARIMA (0, d, 0) with Gaussian and non-Gaussian innovations.
We show significant deviation in the scaling behavior between the empirical
sample and the surrogate counterpart at large time-scales in the case of FARIMA
(0, d, 0) with non-Gaussian innovations whereas no such discrepancy was
observed in the case of Gaussian innovations. The results encourage cautious
interpretation of surrogate testing in the presence of non-Gaussian
innovations.
