Maximum Likelihood Estimation of Generalized Ito Processes with Discretely Sampled Data

Lo, Andrew W.

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Maximum Likelihood Estimation of Generalized Ito Processes with Discretely Sampled Data

NBER Working Paper No. t0059

32 Pages Posted: 27 Jun 2007 Last revised: 1 Mar 2020

See all articles by Andrew W. Lo

Andrew W. Lo

Massachusetts Institute of Technology (MIT) - Laboratory for Financial Engineering

There are 2 versions of this paper

Date Written: August 1986

Abstract

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