additive noise in process [**A**]{} and the multiplicative one in process [**B**]{}. The power-law decay (with exponent $1/(q-1)-\nu$) for large values of $x$ is ruled by either one of the effective coefficients $\gamma_\mu$ (in [**A**]{}) or $\gamma_\alpha$ (in [**B**]{}) (see Table). That is, the tail is fatter, the larger the corresponding noise amplitude. While in process [**A**]{} the multiplicative noise affects the tail, in model [**B**]{} it is affected by the additive noise, oppositely to what happens for small values. This is related to the sign of $s$, indicating higher multiplicative feedback for either increasing ([**A**]{}) or decreasing ([**B**]{}) values of $x$.

Besides the good agreement already observed for volumes [@vol1; @vol2], we tested this functional form to daily data of volatilities reported in the literature[@micciche]. The results are shown in Fig. \[fig:data\]. In the models we are generalizing, the variable $x$ is usually identified with the variance or squared volatility ($x=v^2$). Then, the resulting PDF for $v$ is $$\label{fitvol}
P(v)\;=\;\rho_o \frac{(v/v_o)^{2\nu+1}}
{[1+(q-1)(v/v_o)^2]^\frac{1}{q-1} }  \; ,$$ with $\rho_o={\scriptstyle 2(2-q)^\nu(q-1)\Gamma(\frac{1}{q-1}-1)/
[v_o\Gamma(\nu+1)\Gamma(\frac{1}{q-1}-\nu-1)]}$.

Fig. \[fig:data\] shows an excellent agreement between theoretical and empirical PDFs, for the full range of data. Notice that the very central part of the distribution is parabolic in the 