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We propose a two stage procedure for the estimation of the parameters of afairly general, continuous-time stochastic volatility. An important ingredientof the proposed method is the Cuchiero-Teichmann volatility estimator, which isbased on Fourier transforms and provides a continuous time estimate of thelatent process. This estimate is then used to construct an approximatelikelihood for the parameters of interest, whose restrictions are taken intoaccount through prior distributions. The procedure is shown to be highlysuccessful for constructing the posterior distribution of the parameters of aHeston model, while limited success is achieved when applied to the highlyparametrized exponential-Ornstein-Uhlenbeck.

Bayesian Approach for Parameter Estimation of Continuous-Time Stochastic Volatility Models using Fourier Transform Methods