The Annals of Statistics

Monte Carlo maximum likelihood estimation for discretely observed diffusion processes

Alexandros Beskos, Omiros Papaspiliopoulos, and Gareth Roberts

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Abstract

This paper introduces a Monte Carlo method for maximum likelihood inference in the context of discretely observed diffusion processes. The method gives unbiased and a.s. continuous estimators of the likelihood function for a family of diffusion models and its performance in numerical examples is computationally efficient. It uses a recently developed technique for the exact simulation of diffusions, and involves no discretization error. We show that, under regularity conditions, the Monte Carlo MLE converges a.s. to the true MLE. For datasize n→∞, we show that the number of Monte Carlo iterations should be tuned as $\mathcal{O}(n^{1/2})$ and we demonstrate the consistency properties of the Monte Carlo MLE as an estimator of the true parameter value.

Article information

Source
Ann. Statist. Volume 37, Number 1 (2009), 223-245.

Dates
First available in Project Euclid: 16 January 2009

Permanent link to this document
https://projecteuclid.org/euclid.aos/1232115933

Digital Object Identifier
doi:10.1214/07-AOS550

Mathematical Reviews number (MathSciNet)
MR2488350

Zentralblatt MATH identifier
1169.65004

Subjects
Primary: 65C30: Stochastic differential and integral equations
Secondary: 62M05: Markov processes: estimation

Keywords
Coupling uniform convergence exact simulation linear diffusion processes random function SLLN on Banach space

Citation

Beskos, Alexandros; Papaspiliopoulos, Omiros; Roberts, Gareth. Monte Carlo maximum likelihood estimation for discretely observed diffusion processes. Ann. Statist. 37 (2009), no. 1, 223--245. doi:10.1214/07-AOS550. https://projecteuclid.org/euclid.aos/1232115933.


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