In this thesis we address two problems. In the first part we consider hypoelliptic diffusions, under both strong and weak Hormander condition. We find Gaussian estimates for the density of the law of the solution at a fixed, short time. A main tool to prove these estimates is Malliavin Calculus, in particular some techniques recently developed to deal with degenerate problems. We then use these short-time estimates to show exponential two-sided bounds for the probability that the diffusion remains in a small tube around a deterministic path up to a given time. In our hypoelliptic framework, the shape of the tube must reflect the fact the diffusion moves with a different speed in the direction of the diffusion coefficient and in the direction of the Lie brackets. For this reason we introduce a norm accounting of this anisotropic behavior, which can be adapted to both the strong and weak Hormander framework. We establish a connection between this norm and the standard control distance in the strong Hormander case. In the weak Hormander case, we introduce a suitable equivalent control distance. In the second part of the thesis we work with mean reverting stochastic volatility models, with a volatility driven by a jump process. We first suppose that the jumps follow a Poisson process, and consider the decay of cross asset correlations, both theoretically and empirically. This leads us to study an algorithm for the detection of jumps in the volatility profile. We then consider a more subtle phenomenon widely observed in financial indices: the multiscaling of moments, i.e. the fact that the q-moment of the log-increment of the price on a time lag of length h scales as h to a certain power of q, which is non-linear in q. We work with models where the volatility follows a mean reverting SDE driven by a Lévy subordinator. We show that multiscaling occurs if the characteristic measure of the Lévy has power law tails and the mean reversion is super-linear at infinity. In this case the scaling function is piecewise linear.