Discrete-time stochastic volatility (SV) models have generated a considerable literature in financial econometrics. However, carrying out inference for these models is a difficult task and often relies on carefully customized Markov chain Monte Carlo techniques. Our contribution here is twofold....

Likelihood based estimation of the parameters of state space models can be carried out via a particle filter.  In this paper we show how to make valid inference on such parameters when the model is incorrect.  In particular we develop a simulation strategy for computing sandwich covariance...