  We consider two kinds of stochastic volatility models. Both kinds of models
contain a stationary volatility process, the density of which, at a fixed
instant in time, we aim to estimate.
  We discuss discrete time models where for instance a log price process is
modeled as the product of a volatility process and i.i.d. noise. We also
consider samples of certain continuous time diffusion processes. The sampled
time instants will be be equidistant with vanishing distance.
  A Fourier type deconvolution kernel density estimator based on the logarithm
of the squared processes is proposed to estimate the volatility density.
Expansions of the bias and bounds on the variances are derived.
