Maximum Likelihood Estimation of Generalized Ito Processes with Discretely Sampled Data

Andrew W. Lo

NBER Technical Working Paper No. 59
Issued in August 1986
NBER Program(s):   ME

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.

(1186 K)

Published: Econometric Theory, vol. 4, 1988, pp. 231-247

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