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http://ssrn.com/abstract=994628
 
 

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Maximum Likelihood Estimation of Generalized Ito Processes with Discretely Sampled Data


Andrew W. Lo


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

August 1986

NBER Working Paper No. t0059

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.

Number of Pages in PDF File: 32

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Date posted: June 27, 2007  

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: http://ssrn.com/abstract=994628

Contact Information

Andrew W. Lo (Contact Author)
Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )
100 Main Street
E62-618
Cambridge, MA 02142
United States
617-253-0920 (Phone)
781 891-9783 (Fax)
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Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)
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Cambridge, MA 02142
United States
National Bureau of Economic Research (NBER)
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Cambridge, MA 02138
United States
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