  In this paper we perform Bayesian estimation of stochastic volatility models
with heavy tail distributions using Metropolis adjusted Langevin (MALA) and
Riemman manifold Langevin (MMALA) methods. We provide analytical expressions
for the application of these methods, assess the performance of these
methodologies in simulated data and illustrate their use on two financial time
series data sets.
