We address the problem of parameter estimation for diffusion driven
stochastic volatility models through Markov chain Monte Carlo (MCMC). To avoid
degeneracy issues we introduce an innovative reparametrisation defined through
transformations that operate on the time scale of the diffusion. A novel MCMC
scheme which overcomes the inherent difficulties of time change transformations
is also presented. The algorithm is fast to implement and applies to models
with stochastic volatility. The methodology is tested through simulation based
experiments and illustrated on data consisting of US treasury bill rates