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On a quantile autoregressive conditional duration model applied to high-frequency financial data

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  • Helton Saulo
  • Narayanaswamy Balakrishnan
  • Roberto Vila

Abstract

Autoregressive conditional duration (ACD) models are primarily used to deal with data arising from times between two successive events. These models are usually specified in terms of a time-varying conditional mean or median duration. In this paper, we relax this assumption and consider a conditional quantile approach to facilitate the modeling of different percentiles. The proposed ACD quantile model is based on a skewed version of Birnbaum-Saunders distribution, which provides better fitting of the tails than the traditional Birnbaum-Saunders distribution, in addition to advancing the implementation of an expectation conditional maximization (ECM) algorithm. A Monte Carlo simulation study is performed to assess the behavior of the model as well as the parameter estimation method and to evaluate a form of residual. A real financial transaction data set is finally analyzed to illustrate the proposed approach.

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  • Helton Saulo & Narayanaswamy Balakrishnan & Roberto Vila, 2021. "On a quantile autoregressive conditional duration model applied to high-frequency financial data," Papers 2109.03844, arXiv.org.
    • Handle: RePEc:arx:papers:2109.03844
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    References listed on IDEAS

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    1. Koenker, Roger & Xiao, Zhijie, 2006. "Quantile Autoregression," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 980-990, September.
    2. 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.
    3. Saranjeet Kaur Bhogal & Ramanathan Thekke Variyam, 2019. "Conditional Duration Models For High‐Frequency Data: A Review On Recent Developments," Journal of Economic Surveys, Wiley Blackwell, vol. 33(1), pages 252-273, February.
    4. Yiu-kuen Tse & Thomas Tao Yang, 2012. "Estimation of High-Frequency Volatility: An Autoregressive Conditional Duration Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 533-545, April.
    5. 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).
    6. repec:bla:jecsur:v:22:y:2008:i:4:p:711-751 is not listed on IDEAS
    7. Allen, David & Ng, K.H. & Peiris, Shelton, 2013. "Estimating and simulating Weibull models of risk or price durations: An application to ACD models," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 214-225.
    8. Luis Sánchez & Víctor Leiva & Manuel Galea & Helton Saulo, 2021. "Birnbaum‐Saunders quantile regression and its diagnostics with application to economic data," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 37(1), pages 53-73, January.
    9. Helton Saulo & Jeremias Leão & Víctor Leiva & Robert G. Aykroyd, 2019. "Birnbaum–Saunders autoregressive conditional duration models applied to high-frequency financial data," Statistical Papers, Springer, vol. 60(5), pages 1605-1629, October.
    10. Bhatti, Chad R., 2010. "The Birnbaum–Saunders autoregressive conditional duration model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(10), pages 2062-2078.
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