Sequential Monte Carlo Smoothing with Parameter Estimation

Biao Yang, Jonathan R. Stroud and Gabriel Huerta

George Washington University, Georgetown University and University of New Mexico

April 2016

In this paper, we propose two new Bayesian smoothing methods for general state-space models with unknown parameters. The first approach is based on the particle learning and smoothing algorithm, but with an adjustment in the backwards resampling weights. The second is a new method combining sequential parameter learning and smoothing algorithms for general state-space models. This method is straightforward but effective, and we find it is the best existing sequential Monte Carlo algorithm to solve the joint Bayesian smoothing problem. We first illustrate the methods on three benchmark models using simulated data, and then apply them to a stochastic volatility model for daily S&P 500 index returns during the financial crisis.

The manuscript is available in PDF format.

Supporting Materials