  A self-stabilizing processes $\{Z(t), t\in [t_0,t_1)\}$ is a random process
which when localized, that is scaled to a fine limit near a given $t\in
[t_0,t_1)$, has the distribution of an $\alpha(Z(t))$-stable process, where
$\alpha: \mathbb{R}\to (0,2)$ is a given continuous function. Thus the
stability index near $t$ depends on the value of the process at $t$. In an
earlier paper we constructed self-stabilizing processes using sums over plane
Poisson point processes in the case of $\alpha: \mathbb{R}\to (0,1)$ which
depended on the almost sure absolute convergence of the sums. Here we construct
pure jump self-stabilizing processes when $\alpha$ may take values greater than
1 when convergence may no longer be absolute. We do this in two stages, firstly
by setting up a process based on a fixed point set but taking random signs of
the summands, and then randomizing the point set to get a process with the
desired local properties.
