Fitting general stochastic volatility models using Laplace accelerated sequential importance sampling

Simulated maximum likelihood has proved to be a valuable tool for fitting the log-normal stochastic volatility model to financial returns time series. In this paper, we develop a methodology that generalizes these methods to more general stochastic volatility models that are naturally cast in terms of a positive volatility process. The methodology relies on combining two well known methods for evaluating the likelihood function – Sequential importance sampling and Laplace importance sampling. Two example models are considered, showing that the likelihood function can be evaluated using Monte Carlo methods even for non-Gaussian latent processes such as square-root diffusions. JEL code: C13