S.Pastorello E.Rossi
Efficient
Importance Sampling Maximum Likelihood Estimation of Stochastic Differential
Equations
Quaderni di Dipartimento #171 (12-04)
Dipartimento di economia politica e metodi quantitativi
Università degli studi di Pavia
Abstract
This paper
considers ML estimation of a diffusion process observed discretely. Since the
exact loglikelihood is generally not available, it must be approximated. We
review the most efficient
approaches
in the literature, and point to some drawbacks. We propose to approximate the loglikelihood
using the EIS strategy, and detail its implementation for univariate
homogeneous processes. Some Monte Carlo experiments evaluate its performance against
an alternative IS strategy , showing that EIS is at least equivalent, if not
superior, while allowing a greater flexibility needed when examining more complicated
models.