  We propose a two stage procedure for the estimation of the parameters of a
fairly general, continuous-time stochastic volatility. An important ingredient
of the proposed method is the Cuchiero-Teichmann volatility estimator, which is
based on Fourier transforms and provides a continuous time estimate of the
latent process. This estimate is then used to construct an approximate
likelihood for the parameters of interest, whose restrictions are taken into
account through prior distributions. The procedure is shown to be highly
successful for constructing the posterior distribution of the parameters of a
Heston model, while limited success is achieved when applied to the highly
parametrized exponential-Ornstein-Uhlenbeck.
