  We consider Feller mean-reverting square-root diffusion, which has been
applied to model a wide variety of processes with linearly state-dependent
diffusion, such as stochastic volatility and interest rates in finance, and
neuronal and populations dynamics in natural sciences. We focus on the
statistical mixing (or superstatistical) process in which the parameter related
to the mean value can fluctuate - a plausible mechanism for the emergence of
heavy-tailed distributions. We obtain analytical results for the associated
probability density function (both stationary and time dependent), its
correlation structure and aggregation properties. Our results are applied to
explain the statistics of stock traded volume at different aggregation scales.
