### Maximum likelihood estimation of hidden Markov processes

Halina Frydman and Peter Lakner
Source: Ann. Appl. Probab. Volume 13, Number 4 (2003), 1296-1312.

#### Abstract

We consider the process $dY_{t}=u_{t}\,dt+dW_{t},$ where $u$ is a process not necessarily adapted to $\mathcal{F}^{Y}$ (the filtration generated by the process $Y)$ and $W$ is a Brownian motion. We obtain a general representation for the likelihood ratio of the law of the $Y$ process relative to Brownian measure. This representation involves only one basic filter (expectation of $u$ conditional on observed process $Y$). This generalizes the result of Kailath and Zakai [Ann. Math. Statist. 42 (1971) 130-140] where it is assumed that the process $u$ is adapted to $\mathcal{F}^{Y}.$ In particular, we consider the model in which $u$ is a functional of $Y$ and of a random element $X$ which is independent of the Brownian motion $W.$ For example, $X$ could be a diffusion or a Markov chain. This result can be applied to the estimation of an unknown multidimensional parameter $\theta$ appearing in the dynamics of the process $u$ based on continuous observation of $Y$ on the time interval $[0,T]$. For a specific hidden diffusion financial model in which $u$ is an unobserved mean-reverting diffusion, we give an explicit form for the likelihood function of $\theta.$ For this model we also develop a computationally explicit E--M algorithm for the estimation of $\theta.$ In contrast to the likelihood ratio, the algorithm involves evaluation of a number of filtered integrals in addition to the basic filter.

First Page:
Primary Subjects: 62M05, 60J60, 60J25
Full-text: Open access

Permanent link to this document: http://projecteuclid.org/euclid.aoap/1069786500
Digital Object Identifier: doi:10.1214/aoap/1069786500
Mathematical Reviews number (MathSciNet): MR2023878
Zentralblatt MATH identifier: 1035.62084

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