Inference for Non-linear Diffusions and Jump-Diffusions: A Monte Carlo EM approach
We propose a simple, general and computationally efficient algorithm for maximum likelihood estima- tion (MLE) of parameters in diffusion and jump-diffusion processes. This is conducted within a Monte Carlo EM-algorithm, where the smoothing distribution is computed using resampling. The results are encouraging as we can approximate the MLE well for the models studied when using simulated data. We