Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/87103
Authors: 
Pastorello, Sergio
Rossi, Eduardo
Year of Publication: 
2004
Series/Report no.: 
Quaderni di Dipartimento, EPMQ, Università degli Studi di Pavia 171
Abstract: 
This paper considers ML estimation of a diffusion process observed discretely. Since the exact loglikelihood is generally not available, it must be approximated. We review the most effcient approaches in the literature, and point to some drawbacks. We propose to approximate the loglikelihood using the EIS strategy (Richard and Zhang, 1998), and detail its implementation for univariate homogeneous processes. Some Monte Carlo experiments evaluate its performance against an alternative IS strategy (Durham and Gallant, 2002), showing that EIS is at least equivalent, if not superior, while allowing a greater exibility needed when examining more complicated models.
Subjects: 
Diffusion process
Stochastic differential equation
Transition density
Importance sampling
Simulated maximum likelihood
JEL: 
C13
C15
C22
Document Type: 
Working Paper

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