32 Pages Posted: 27 Jun 2007
Date Written: August 1986
In this paper, we consider the parametric estimation problem for continuous time stochastic processes described by general first-order nonlinear stochastic differential equations of the Ito type. We characterize the likelihood function of a discretely-sampled set of observations as the solution to a functional partial differential equation. The consistency and asymptotic normality of the maximum likelihood estimators are explored, and several illustrative examples are provided.
Suggested Citation: Suggested Citation
Lo, Andrew W., Maximum Likelihood Estimation of Generalized Ito Processes with Discretely Sampled Data (August 1986). NBER Working Paper No. t0059. Available at SSRN: https://ssrn.com/abstract=994628
By Andrew Lo