Maximum Likelihood Estimation of Generalized Ito Processes with Discretely Sampled Data

32 Pages Posted: 3 Jul 2007 Last revised: 1 Sep 2010

See all articles by Andrew W. Lo

Andrew W. Lo

Massachusetts Institute of Technology (MIT) - Sloan School of Management; National Bureau of Economic Research (NBER); Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)

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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

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=579749

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