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Maximum likelihood estimation for integrated diffusion processes

  • Fernando Baltazar-Larios

    ()

    (Universidad Nacional Autónoma de México)

  • Michael Sørensen

    ()

    (University of Copenhagen and CREATES)

We propose a method for obtaining maximum likelihood estimates of parameters in diffusion models when the data is a discrete time sample of the integral of the process, while no direct observations of the process itself are available. The data are, moreover, assumed to be contaminated by measurement errors. Integrated volatility is an example of this type of observations. Another example is ice-core data on oxygen isotopes used to investigate paleo-temperatures. The data can be viewed as incomplete observations of a model with a tractable likelihood function. Therefore we propose a simulated EM-algorithm to obtain maximum likelihood estimates of the parameters in the diffusion model. As part of the algorithm, we use a recent simple method for approximate simulation of diffusion bridges. In simulation studies for the Ornstein-Uhlenbeck process and the CIR process the proposed method works well.

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File URL: ftp://ftp.econ.au.dk/creates/rp/10/rp10_33.pdf
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Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2010-33.

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Length: 16
Date of creation: 05 Aug 2010
Date of revision:
Handle: RePEc:aah:create:2010-33
Contact details of provider: Web page: http://www.econ.au.dk/afn/

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