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A nonparametric ACD model

Author

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  • COSMA, Antonio
  • GALLI, Fausto

Abstract

We carry out a nonparametric analysis of financial durations. We make use of an existing algorithm to describe nonparametrically the dynamics of the process in terms of its lagged realizations and of a latent variable, its conditional mean. The devices needed to effectively apply the algorithm to our dataset are presented. On simulated data, the nonparametric procedure yields better estimates than the ones delivered by an incorrectly specified parametric method. On a real dataset, the nonparametric analysis can convey information on the nature of the data generating process that may not be captured by the parametric specification. In this view, the nonparametric method proposed can be a valuable preliminary analysis able to suggest the choice of a 'good' parametric specification, or a complement of a parametric estimation."

Suggested Citation

  • COSMA, Antonio & GALLI, Fausto, 2006. "A nonparametric ACD model," LIDAM Discussion Papers CORE 2006067, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvco:2006067
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    File URL: https://sites.uclouvain.be/core/publications/coredp/coredp2006.html
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    Cited by:

    1. Patrick W. Saart & Jiti Gao & David E. Allen, 2015. "Semiparametric Autoregressive Conditional Duration Model: Theory and Practice," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 849-881, December.

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    Keywords

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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