  In this paper we introduce a new parametric distribution, the Mixed Tempered
Stable. It has the same structure of the Normal Variance Mean Mixtures but the
normality assumption leaves place to a semi-heavy tailed distribution. We show
that, by choosing appropriately the parameters of the distribution and under
the concrete specification of the mixing random variable, it is possible to
obtain some well-known distributions as special cases.
  We employ the Mixed Tempered Stable distribution which has many attractive
features for modeling univariate returns. Our results suggest that it is enough
flexible to accomodate different density shapes. Furthermore, the analysis
applied to statistical time series shows that our approach provides a better
fit than competing distributions that are common in the practice of finance.
