  We consider a continuous-time stochastic volatility model. The model contains
a stationary volatility process, the multivariate density of the finite
dimensional distributions of which we aim to estimate. We assume that we
observe the process at discrete instants in time. The sampling times will be
equidistant with vanishing distance.
  A multivariate Fourier-type deconvolution kernel density estimator based on
the logarithm of the squared processes is proposed to estimate the multivariate
volatility density. An expansion of the bias and a bound on the variance are
derived.
  Key words: stochastic volatility models, multivariate density estimation,
kernel estimator, deconvolution, mixing
