IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v124y2014i3p362-366.html
   My bibliography  Save this article

A semiparametric conditional duration model

Author

Listed:
  • Dungey, Mardi
  • Long, Xiangdong
  • Ullah, Aman
  • Wang, Yun

Abstract

We propose a new semiparametric autoregressive duration (SACD) model, which incorporates the parametric and nonparametric estimators of the conditional duration in a multiplicative way. Asymptotic properties for this combined estimator are presented. The empirical application to the transaction duration of the US 2-Year Treasury note shows the outperformance of our SACD models over parametric ACD models.

Suggested Citation

  • Dungey, Mardi & Long, Xiangdong & Ullah, Aman & Wang, Yun, 2014. "A semiparametric conditional duration model," Economics Letters, Elsevier, vol. 124(3), pages 362-366.
  • Handle: RePEc:eee:ecolet:v:124:y:2014:i:3:p:362-366
    DOI: 10.1016/j.econlet.2014.06.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176514002249
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.econlet.2014.06.013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    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. Lee, Sang-Won & Hansen, Bruce E., 1994. "Asymptotic Theory for the Garch(1,1) Quasi-Maximum Likelihood Estimator," Econometric Theory, Cambridge University Press, vol. 10(1), pages 29-52, March.
    4. Carlos Martins-Filho & Santosh Mishra & Aman Ullah, 2008. "A Class of Improved Parametrically Guided Nonparametric Regression Estimators," Econometric Reviews, Taylor & Francis Journals, vol. 27(4-6), pages 542-573.
    5. 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.
    6. Luc Bauwens & Pierre Giot, 2000. "The Logarithmic ACD Model: An Application to the Bid-Ask Quote Process of Three NYSE Stocks," Annals of Economics and Statistics, GENES, issue 60, pages 117-149.
    7. Lumsdaine, Robin L, 1996. "Consistency and Asymptotic Normality of the Quasi-maximum Likelihood Estimator in IGARCH(1,1) and Covariance Stationary GARCH(1,1) Models," Econometrica, Econometric Society, vol. 64(3), pages 575-596, May.
    8. repec:adr:anecst:y:2000:i:60:p:05 is not listed on IDEAS
    9. Joachim Grammig & Kai-Oliver Maurer, 2000. "Non-monotonic hazard functions and the autoregressive conditional duration model," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 16-38.
    10. Madhavan, Ananth, 2000. "Market microstructure: A survey," Journal of Financial Markets, Elsevier, vol. 3(3), pages 205-258, August.
    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. Mardi Dungey & Olan Henry & Michael Mckenzie, 2013. "Modeling trade duration in U.S. Treasury markets," Quantitative Finance, Taylor & Francis Journals, vol. 13(9), pages 1431-1442, September.
    13. 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.
    14. Goodhart, Charles A. E. & O'Hara, Maureen, 1997. "High frequency data in financial markets: Issues and applications," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 73-114, June.
    15. Zhang, Michael Yuanjie & Russell, Jeffrey R. & Tsay, Ruey S., 2001. "A nonlinear autoregressive conditional duration model with applications to financial transaction data," Journal of Econometrics, Elsevier, vol. 104(1), pages 179-207, August.
    16. Long, Xiangdong & Su, Liangjun & Ullah, Aman, 2011. "Estimation and Forecasting of Dynamic Conditional Covariance: A Semiparametric Multivariate Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 109-125.
    17. Mishra, Santosh & Su, Liangjun & Ullah, Aman, 2010. "Semiparametric Estimator of Time Series Conditional Variance," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 256-274.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Vasileios Siakoulis & Ioannis Venetis, 2015. "On inter-arrival times of bond market extreme events. An application to seven European markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(4), pages 717-741, October.
    3. Siakoulis, Vasilios, 2015. "Modeling bank default intensity in the USA using autoregressive duration models," MPRA Paper 64526, University Library of Munich, Germany.
    4. Allen, David & Lazarov, Zdravetz & McAleer, Michael & Peiris, Shelton, 2009. "Comparison of alternative ACD models via density and interval forecasts: Evidence from the Australian stock market," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2535-2555.
    5. Fernandes, Marcelo & Grammig, Joachim, 2005. "Nonparametric specification tests for conditional duration models," Journal of Econometrics, Elsevier, vol. 127(1), pages 35-68, July.
    6. Katarzyna Bien-Barkowska, 2011. "Distribution Choice for the Asymmetric ACD Models," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 11, pages 55-72.
    7. 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.
    8. 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 Jan 2022.
    9. 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.
    10. 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.
    11. Perera, Indeewara & Silvapulle, Mervyn J., 2021. "Bootstrap based probability forecasting in multiplicative error models," Journal of Econometrics, Elsevier, vol. 221(1), pages 1-24.
    12. Ma, Boyuan & Chu, Tingjin & Jin, Zhuo, 2022. "Frequency and severity estimation of cyber attacks using spatial clustering analysis," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 33-45.
    13. Wei Sun & Svetlozar Rachev & Frank Fabozzi & Petko Kalev, 2008. "Fractals in trade duration: capturing long-range dependence and heavy tailedness in modeling trade duration," Annals of Finance, Springer, vol. 4(2), pages 217-241, March.
    14. Saulo, Helton & Balakrishnan, Narayanaswamy & Vila, Roberto, 2023. "On a quantile autoregressive conditional duration model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 203(C), pages 425-448.
    15. Bhatti, Chad R., 2010. "The Birnbaum–Saunders autoregressive conditional duration model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(10), pages 2062-2078.
    16. Xiufeng Yan, 2021. "Autoregressive conditional duration modelling of high frequency data," Papers 2111.02300, arXiv.org.
    17. Zhang, Yaohua & Zou, Jian & Ravishanker, Nalini & Thavaneswaran, Aerambamoorthy, 2019. "Modeling financial durations using penalized estimating functions," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 145-158.
    18. Nikolaus Hautsch & Peter Malec & Melanie Schienle, 2014. "Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 12(1), pages 89-121.
    19. Stanislav Anatolyev & Dmitry Shakin, 2006. "Trade intensity in the Russian stock market:dynamics, distribution and determinants," Working Papers w0070, Center for Economic and Financial Research (CEFIR).
    20. Yiing Fei Tan & Kok Haur Ng & You Beng Koh & Shelton Peiris, 2022. "Modelling Trade Durations Using Dynamic Logarithmic Component ACD Model with Extended Generalised Inverse Gaussian Distribution," Mathematics, MDPI, vol. 10(10), pages 1-20, May.

    More about this item

    Keywords

    Duration; Nonparametric estimator; Semiparametric model;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G0 - Financial Economics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecolet:v:124:y:2014:i:3:p:362-366. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.