Exact fast smoothing in switching models with application to stochastic volatility

We consider the problem of statistical smoothing in nonlinear non-Gaussian systems. Our novel method relies on a Markov-switching model to operate recursively on series of noisy input data to produce an estimate of the underlying system state. We show through a set of experiments that our technique is efficient within the framework of the stochastic volatility model.