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Testing the Conditional Mean Function of Autoregressive Conditional Duration Models

Author

Listed:
  • Nikolaus Hautsch

    (Department of Economics, University of Copenhagen)

Abstract
This paper proposes a dynamic proportional hazard (PH) model with non-specified baseline hazard for the modelling of autoregressive duration processes. A categorization of the durations allows us to reformulate the PH model as an ordered response model based on extreme value distributed errors. In order to capture persistent serial dependence in the duration process, we extend the model by an observation driven ARMA dynamic based on generalized errors. We illustrate the maximum likelihood estimation of both the model parameters and discrete points of the underlying unspecified baseline survivor function. The dynamic properties of the model as well as an assessment of the estimation quality is investigated in a Monte Carlo study. It is illustrated that the model is a useful approach to estimate conditional failure probabilities based on (persistent) serial dependent duration data which might be subject to censoring structures. In an empirical study based on financial transaction data we present an application of the model to estimate conditional asset price change probabilities. Evaluating the forecasting properties of the model, it is shown that the proposed approach is a promising competitor to well-established ACD type models.

Suggested Citation

  • Nikolaus Hautsch, 2006. "Testing the Conditional Mean Function of Autoregressive Conditional Duration Models," FRU Working Papers 2006/06, University of Copenhagen. Department of Economics. Finance Research Unit.
    • Handle: RePEc:kud:kuiefr:200606
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    File URL: http://www.econ.ku.dk/FRU/WorkingPapers/PDF/2006/2006_06.pdf
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    Citations

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    Cited by:

    1. Luc, BAUWENS & Nikolaus, HAUTSCH, 2006. "Modelling Financial High Frequency Data Using Point Processes," Discussion Papers (ECON - Département des Sciences Economiques) 2006039, Université catholique de Louvain, Département des Sciences Economiques.
    2. BAUWENS, Luc & HAUTSCH, Nikolaus, 2003. "Dynamic latent factor models for intensity processes," LIDAM Discussion Papers CORE 2003103, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Indeewara Perera & Javier Hidalgo & Mervyn J. Silvapulle, 2016. "A Goodness-of-Fit Test for a Class of Autoregressive Conditional Duration Models," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 1111-1141, June.
    4. Hautsch, Nikolaus, 2008. "Capturing common components in high-frequency financial time series: A multivariate stochastic multiplicative error model," Journal of Economic Dynamics and Control, Elsevier, vol. 32(12), pages 3978-4015, December.
    5. Hautsch, Nikolaus & Jeleskovic, Vahidin, 2008. "Modelling high-frequency volatility and liquidity using multiplicative error models," SFB 649 Discussion Papers 2008-047, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    6. Dungey, Mardi & Jeyasreedharan, Nagaratnam & Li, Tuo, 2010. "Modelling the Time Between Trades in the After-Hours Electronic Equity Futures Market," Working Papers 10451, University of Tasmania, Tasmanian School of Business and Economics, revised 30 May 2012.
    7. Dungey, Mardi & Henry, Olan & McKenzie, Michael, 2010. "From Trade-to-Trade in US Treasuries," Working Papers 10446, University of Tasmania, Tasmanian School of Business and Economics, revised 01 May 2010.
    8. repec:hum:wpaper:sfb649dp2008-047 is not listed on IDEAS
    9. repec:bla:jecsur:v:22:y:2008:i:4:p:711-751 is not listed on IDEAS
    10. repec:wyi:journl:002120 is not listed on IDEAS
    11. Yongmiao Hong & Yoon-Jin Lee, 2007. "Detecting Misspecifications in Autoregressive Conditional Duration Models," CAEPR Working Papers 2007-019, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    12. Allen, David & Chan, Felix & McAleer, Michael & Peiris, Shelton, 2008. "Finite sample properties of the QMLE for the Log-ACD model: Application to Australian stocks," Journal of Econometrics, Elsevier, vol. 147(1), pages 163-185, November.

    More about this item

    Keywords

    augmented ACD models; semiparametric ACD models; news impact function; Lagrange multiplier tests;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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