Maximum Likelihood Estimation of Generalized Itô Processes with Discretely Sampled Data

Andrew Lo ()

Econometric Theory, 1988, vol. 4, issue 2, 231-247

Abstract: This paper considers the parametric estimation problem for continuous-time stochastic processes described by first-order nonlinear stochastic differential equations of the generalized Itô type (containing both jump and diffusion components). We derive a particular functional partial differential equation which characterizes the exact likelihood function of a discretely sampled Itô process. In addition, we show by a simple counterexample that the common approach of estimating parameters of an Itô process by applying maximum likelihood to a discretization of the stochastic differential equation does not yield consistent estimators.

Date: 1988
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Related works:
Working Paper: Maximum Likelihood Estimation of Generalized Ito Processes with Discretely Sampled Data (1986)
Working Paper: Maximum Likelihood Estimation of Generalized Ito Processes with Discretely Sampled Data
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