Rong Chen
Dept of Statistics
Rutgers University
Title: Statistical inferences of diffusion process with Sequential Monte Carlo
In financial markets and other applications, continuous time diffusion processes are often
observed at discrete time. For nonlinear processes, the likelihood function and posterior
density of the parameters are often much easier to evaluate with continuously observed
paths of the process than with discretely observed paths. In this paper we propose to use a
modified version of the sequential Monte Carlo method to sample continuous diffusion
bridges based on discretely observed observations. Statistical inferences are then made
with the simulated diffusion bridges. Empirical study and real applications are presented.