Beyond the normal approximation: robust filtering and smoothing via perturbation methods

Abstract

Using a perturbation technique, we derive a new approximate filtering and smoothing methodology generalizing along different directions several existing approaches to robust filtering based on the score and the Hessian matrix of the observation density. The main advantages of the methodology can be summarized as follows: (i) it relaxes the critical assumption of a Gaussian conditional distribution for the latent states underlying such approaches; (ii) can be applied to a general class of state-space models including location, scale and count data models; (iii) rationalizes the approximation to the likelihood function within the same perturbation approach, thus allowing for straightforward inference of the model parameters; (iv) enables the computation of confidence bands around the state estimates reflecting the combination of parameter and filtering uncertainty. We show through an extensive Monte Carlo study that the mean square loss with respect to exact simulation-based methods is small in a wide range of scenarios. We finally illustrate empirically the application of the methodology to the estimation of stochastic volatility and correlations in financial time-series.

Keywords: Nonlinear filtering, time-varying parameters, stochastic volatility, dynamic correlations

JEL Classification: C22, C32, C58