Consistency of MLEs for partially observed diffusions, with application in market microstructure modelling

Abstract This paper presents a tractable sufficient condition for the consistency of maximum likelihood estimators (MLEs) in partially observed diffusion models, stated in terms of the stationary distribution of the associated fully observed diffusion, under the assumption that the set of unknown parameter values is finite. This sufficient condition is then verified in the context of a latent price model of market microstructure, yielding consistency of maximum likelihood estimators of the unknown parameters in this model. Finally, we compute the latter estimators using historical financial data taken from the NASDAQ exchange.

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Publication Details

Journal
Finance and Stochastics
Published
2026-11-09
DOI
https://doi.org/10.1007/s00780-026-00607-6
Citations
4
Primary Topic
Stochastic processes and financial applications
Type
article
Field-Weighted Citation Impact
7.42

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article

Consistency of MLEs for partially observed diffusions, with application in market microstructure modelling

Sergey Nadtochiy
4 citations
Finance and Stochastics
Stochastic processes and financial applications
7.42
article

Consistency of MLEs for partially observed diffusions, with application in market microstructure modelling

Sergey Nadtochiy
article en
4 citations

Abstract

Abstract This paper presents a tractable sufficient condition for the consistency of maximum likelihood estimators (MLEs) in partially observed diffusion models, stated in terms of the stationary distribution of the associated fully observed diffusion, under the assumption that the set of unknown parameter values is finite. This sufficient condition is then verified in the context of a latent price model of market microstructure, yielding consistency of maximum likelihood estimators of the unknown parameters in this model. Finally, we compute the latter estimators using historical financial data taken from the NASDAQ exchange.

Finance and Stochastics
Illinois Institute of Technology (US), Carnegie Mellon University (US)
National Science Foundation
Openalex Percentile: Top 5%
Stochastic processes and financial applications
7.42
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