  We consider stochastic processes $Y(t)$ which can be represented as
$Y(t)=(X(t))^s, s \in \mathbb{N},$ where $X(t)$ is a stationary strictly
sub-Gaussian process and build a wavelet-based model that simulates $Y(t)$ with
given accuracy and reliability in $L_p([0,T])$.
  A model for simulation with given accuracy and reliability in $L_p([0,T])$ is
also built for processes $Z(t)$ which can be represented as $Z(t)=X_1(t)
X_2(t)$, where $X_1(t)$ and $X_2(t)$ are independent stationary strictly
sub-Gaussian processes.
