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A non parametric ACD model

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  • Cosma, Antonio
  • Galli, Fausto

Abstract

We carry out a non parametric analysis of financial durations. We make use of an existing algorithm to describe non parametrically 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 non parametric procedure yields better estimates than the ones delivered by an incorrectly specified parametric method. On a real dataset, the non parametric estimator seems to mildly overperform with respect to its parametric counterpart. Moreover the non parametric analysis can convey information on the nature of the data generating process that may not be captured by the parametric specification. In particular, once intraday seasonality is directly used as an explana- tory variable, the non parametric approach provides insights about the time-varying nature of the dynamics in the model that the standard procedures of deseasonaliza- tion may lead to overlook.

Suggested Citation

  • Cosma, Antonio & Galli, Fausto, 2014. "A non parametric ACD model," MPRA Paper 53990, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:53990
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    References listed on IDEAS

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    1. Giovanni Luca & Giampiero Gallo, 2009. "Time-Varying Mixing Weights in Mixture Autoregressive Conditional Duration Models," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 102-120.
    2. Fernandes, Marcelo & Grammig, Joachim, 2006. "A family of autoregressive conditional duration models," Journal of Econometrics, Elsevier, vol. 130(1), pages 1-23, January.
    3. Brownlees, Christian T. & Gallo, Giampiero M., 2011. "Shrinkage estimation of semiparametric multiplicative error models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 365-378, April.
    4. De Luca, Giovanni & Zuccolotto, Paola, 2006. "Regime-switching Pareto distributions for ACD models," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2179-2191, December.
    5. Ghysels, Eric & Gourieroux, Christian & Jasiak, Joann, 2004. "Stochastic volatility duration models," Journal of Econometrics, Elsevier, vol. 119(2), pages 413-433, April.
    6. Bauwens, Luc & Giot, Pierre & Grammig, Joachim & Veredas, David, 2004. "A comparison of financial duration models via density forecasts," International Journal of Forecasting, Elsevier, vol. 20(4), pages 589-609.
    7. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    8. Maria Pacurar, 2008. "Autoregressive Conditional Duration Models In Finance: A Survey Of The Theoretical And Empirical Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 22(4), pages 711-751, September.
    9. BAUWENS, Luc & VEREDAS, David, 1999. "The stochastic conditional duration model: a latent factor model for the analysis of financial durations," LIDAM Discussion Papers CORE 1999058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    10. Gerhard Frank & Hautsch Nikolaus, 2007. "A Dynamic Semiparametric Proportional Hazard Model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 11(2), pages 1-42, May.
    11. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    12. Buhlmann, Peter & McNeil, Alexander J., 2002. "An algorithm for nonparametric GARCH modelling," Computational Statistics & Data Analysis, Elsevier, vol. 40(4), pages 665-683, October.
    13. Luc Bauwens & David Veredas & Winfried Pohlmeier, 2005. "High frequency finance," ULB Institutional Repository 2013/136220, ULB -- Universite Libre de Bruxelles.
    14. Christian T. Brownlees & Fabrizio Cipollini & Giampiero M. Gallo, 2011. "Multiplicative Error Models," Econometrics Working Papers Archive 2011_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Apr 2011.
    15. Engle, Robert F & Gonzalez-Rivera, Gloria, 1991. "Semiparametric ARCH Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(4), pages 345-359, October.
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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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    More about this item

    Keywords

    non parametric; ACD; trade durations; local-linear;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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