  In this paper, we derive a valid Edgeworth expansions for the Bessel
corrected empirical variance when data are generated by a strongly mixing
process whose distribution can be arbitrarily. The constraint of strongly
mixing process makes the problem not easy. Indeed, even for a strongly mixing
normal process, the distribution is unknown. Here, we do not assume any other
assumption than a sufficiently fast decrease of the underlying distribution to
make the Edgeworth expansion convergent. This results can obviously apply to
strongly mixing normal process and provide an alternative to the work of
Moschopoulos (1985) and Mathai (1982).
