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On the (Intradaily) Seasonality and Dynamics of a Financial Point Process : A Semiparametric Approach

Author

Listed:
  • David Veredas

    (Crest)

  • Juan Rodriguez-Poo

    (Crest)

  • Antoni Espasa

    (Crest)

Abstract

A new method of estimating a component model for the analysis of financial durations is proposed. The components are long-run dynamics and seasonality. The latter is left unspecified and the former is assumed to fall within the class ofa certain family of parametric functions. The proposed estimation procedure is based on a generalized profile likelihood approach and requires the assumption either of a likelihood function for the model errors or, at least, that the errordensity belongs to the class of exponential densities. Its main interest is twofold: first, consistent and asymptotically normal estimators for both the parameters of the long-run stochastic component and the nonparametric curve that approximatesthe deterministic seasonal component are provided. Hence, it is possible to derive correct inference for both parametric and nonparametric components. Second, the method is computationally very appealing since the resulting nonparametricestimator of the seasonal curve has an explicit form that turns out to be a transformation of the Nadaraya-Watson estimator. The method is applied to price and volume durations of a stock traded at the NYSE, and compared to estimation with splines and with adjustment methods. It is shown that the proposed method outperforms the other methods.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • David Veredas & Juan Rodriguez-Poo & Antoni Espasa, 2001. "On the (Intradaily) Seasonality and Dynamics of a Financial Point Process : A Semiparametric Approach," Working Papers 2001-19, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2001-19
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    Citations

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    Cited by:

    1. Hautsch, Nikolaus & Pohlmeier, Winfried, 2001. "Econometric Analysis of Financial Transaction Data: Pitfalls and Opportunities," CoFE Discussion Papers 01/05, University of Konstanz, Center of Finance and Econometrics (CoFE).
    2. Hujer, Reinhard & Vuletic, Sandra, 2007. "Econometric analysis of financial trade processes by discrete mixture duration models," Journal of Economic Dynamics and Control, Elsevier, vol. 31(2), pages 635-667, February.
    3. Fernandes, Marcelo & Grammig, Joachim, 2005. "Nonparametric specification tests for conditional duration models," Journal of Econometrics, Elsevier, vol. 127(1), pages 35-68, July.
    4. Xiufeng Yan, 2021. "Autoregressive conditional duration modelling of high frequency data," Papers 2111.02300, arXiv.org.
    5. Roman Huptas, 2014. "Bayesian Estimation and Prediction for ACD Models in the Analysis of Trade Durations from the Polish Stock Market," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 6(4), pages 237-273, December.
    6. Hautsch, Nikolaus, 2002. "Modelling Intraday Trading Activity Using Box-Cox-ACD Models," CoFE Discussion Papers 02/05, University of Konstanz, Center of Finance and Econometrics (CoFE).
    7. Ben Omrane, Walid & de Bodt, Eric, 2007. "Using self-organizing maps to adjust for intra-day seasonality," Journal of Banking & Finance, Elsevier, vol. 31(6), pages 1817-1838, June.
    8. BAUWENS, Luc & GALLI, Fausto & GIOT, Pierre, 2003. "The moments of Log-ACD models," LIDAM Discussion Papers CORE 2003011, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    9. Tomoki Toyabe & Teruo Nakatsuma, 2022. "Stochastic Conditional Duration Model with Intraday Seasonality and Limit Order Book Information," JRFM, MDPI, vol. 15(10), pages 1-25, October.
    10. Xiufeng Yan, 2021. "Multiplicative Component GARCH Model of Intraday Volatility," Papers 2111.02376, arXiv.org.
    11. 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.
    12. Francisco Blasques & Vladim'ir Hol'y & Petra Tomanov'a, 2018. "Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros," Papers 1812.07318, arXiv.org, revised May 2024.

    More about this item

    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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