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Estimating doubly stochastic Poisson process with affine intensities by Kalman filter

Listed author(s):
  • Alan Genaro

    ()

  • Adilson Simonis
Registered author(s):

    This paper proposes a Kalman filter formulation for parameter estimation of doubly stochastic Poisson processes (DSPP) with stochastic affine intensities. To achieve this aim, an analytical expression for the probability distribution functions of the corresponding DSPP for any intensity from the class of affine diffusions is obtained. More detailed results are provided for one- and two-factor Feller and Ornstein–Uhlenbeck diffusions. A Monte Carlo study indicates that the proposed method is a reliable procedure for moderate sample sizes. An empirical analysis of one- and two-factor Feller and Ornstein–Uhlenbeck models is carried out using high frequency transaction data. Copyright Springer-Verlag Berlin Heidelberg 2015

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    File URL: http://hdl.handle.net/10.1007/s00362-014-0606-6
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    Article provided by Springer in its journal Statistical Papers.

    Volume (Year): 56 (2015)
    Issue (Month): 3 (August)
    Pages: 723-748

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    Handle: RePEc:spr:stpapr:v:56:y:2015:i:3:p:723-748
    DOI: 10.1007/s00362-014-0606-6
    Contact details of provider: Web page: http://www.springer.com

    Order Information: Web: http://www.springer.com/statistics/business/journal/362

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