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Semiparametric Duration Models

Author

Listed:
  • Drost, F.C.

    (Tilburg University, Center For Economic Research)

  • Werker, B.J.M.

    (Tilburg University, Center For Economic Research)

Abstract

In this article we consider semiparametric duration models and efficient estimation of the parameters in a non-iid environment. In contrast to classical time series models where innovations are assumed to be iid we show that in, for example, the often-used autoregressive conditional duration (ACD) model, the assumption of independent innovations is too restrictive to describe financial durations accurately. Therefore, we consider semiparametric extensions of the standard specification that allow for arbitrary kinds of dependencies between the innovations. The exact nonparametric specification of these dependencies determines the flexibility of the semiparametric model. We calculate semiparametric efficiency bounds for the ACD parameters, discuss the construction of efficient estimators, and study the efficiency loss of the exponential pseudolikelihood procedure. This efficiency loss proves to be sizeable in applications. For durations observed on the Paris Bourse for the Alcatel stock in July and August 1996, the proposed semiparametric procedures clearly outperform pseudolikelihood procedures. We analyze these efficiency gains using a simulation study confirming that, at least at the Paris Bourse, dependencies among rescaled durations can be exploited.
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Suggested Citation

  • Drost, F.C. & Werker, B.J.M., 2001. "Semiparametric Duration Models," Discussion Paper 2001-11, Tilburg University, Center for Economic Research.
  • Handle: RePEc:tiu:tiucen:845b71c6-9525-4006-a0df-40c521c063b0
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    File URL: https://pure.uvt.nl/ws/portalfiles/portal/538393/11.pdf
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    References listed on IDEAS

    as
    1. Ghysels, Eric & Gourieroux, Christian & Jasiak, Joann, 2004. "Stochastic volatility duration models," Journal of Econometrics, Elsevier, vol. 119(2), pages 413-433, April.
    2. Christian Gouriéroux & Joann Jasiak, 2002. "Nonlinear Autocorrelograms: an Application to Inter‐Trade Durations," Journal of Time Series Analysis, Wiley Blackwell, vol. 23(2), pages 127-154, March.
    3. Steigerwald, Douglas G., 1992. "Adaptive estimation in time series regression models," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 251-275.
    4. Drost, F.C. & Klaasens, C.A.J. & Werker, B.J.M., 1994. "Adaptive Estimation in Time Series Models," Papers 9488, Tilburg - Center for Economic Research.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    adaptiveness; durations; one-step improvement; semiparametric efficiency;

    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

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