  We study a new parametric approach for hidden discrete-time diffusion models.
This method is based on contrast minimization and deconvolution and leads to
estimate a large class of stochastic models with nonlinear drift and nonlinear
diffusion. It can be applied, for example, for ecological and financial state
space models. After proving consistency and asymptotic normality of the
estimation, leading to asymptotic confidence intervals, we provide a thorough
numerical study, which compares many classical methods used in practice (Non
Linear Least Square estimator, Monte Carlo Expectation Maxi-mization Likelihood
estimator and Bayesian estimators) to estimate stochastic volatility model. We
prove that our estimator clearly outperforms the Maximum Likelihood Estimator
in term of computing time, but also most of the other methods. We also show
that this contrast method is the most stable and also does not need any tuning
parameter.
