  Surrogate Data Analysis (SDA) is a statistical hypothesis testing framework
for the determination of weak chaos in time series dynamics. Existing SDA
procedures do not account properly for the rich structures observed in stock
return sequences, attributed to the presence of heteroscedasticity, seasonal
effects and outliers. In this paper we suggest a modification of the SDA
framework, based on the robust estimation of location and scale parameters of
mean-stationary time series and a probabilistic framework which deals with
outliers. A demonstration on the NASDAQ Composite index daily returns shows
that the proposed approach produces surrogates that faithfully reproduce the
structure of the original series while being manifestations of linear-random
dynamics.
