  We study a new parametric approach for particular hidden stochastic models
such as the Stochastic Volatility model. This method is based on contrast
minimization and deconvolution. After proving consistency and asymptotic
normality of the estimation leading to asymptotic confidence intervals, we
provide a thorough numerical study, which compares most of the classical
methods that are used in practice (Quasi Maximum Likelihood estimator,
Simulated Expectation Maximization Likelihood estimator and Bayesian
estimators). 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 robust with respect
to non Gaussianity of the error and also does not need any tuning parameter.
