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The ACD Model: Predictability of the Time Between Concecutive Trades

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Author Info

  • Alfonso Dufour

    ()
    (ICMA Centre, University of Reading)

  • Robert F Engle

    (Department of Economics - University of California)

Abstract

Forecasting ability of several parameterizations of ACD models are compared to benchmark linear autoregressions for inter-trade durations. The estimation of parametric ACD models requires both the choice of a conditional density for durations and the specification of a functional form for the conditional mean duration. Our results provide guidance for choosing among different parameterizations and for developing better forecasting models to predict one-step-ahead, multi-step-ahead, and the whole density of time durations. For evaluating density forecasts, we propose a new constructive test, which is based on the series of probability integral transforms. The choice of the conditional distribution for inter-trade durations does not seem to affect the out-of sample performances of the ACD at short, as well as longer, horizons. Yet, this choice becomes critical when forecasting the density.

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Bibliographic Info

Paper provided by Henley Business School, Reading University in its series ICMA Centre Discussion Papers in Finance with number icma-dp2000-05.

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Length: 59 pages
Date of creation: May 2000
Date of revision:
Handle: RePEc:rdg:icmadp:icma-dp2000-05

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  1. Eric Ghysels & Christian Gouriéroux & Joanna Jasiak, 1995. "Trading Patterns, Time Deformation and Stochastic Volatility in Foreign Exchange Markets," CIRANO Working Papers 95s-42, CIRANO.
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  6. Kenneth D. West & Whitney K. Newey, 1995. "Automatic Lag Selection in Covariance Matrix Estimation," NBER Technical Working Papers 0144, National Bureau of Economic Research, Inc.
  7. Dufour, Alfonso & Engle, Robert F, 1999. "Time and the Price Impact of a Trade," University of California at San Diego, Economics Working Paper Series qt62c0h04j, Department of Economics, UC San Diego.
  8. Mitchell, Mark L & Mulherin, J Harold, 1994. " The Impact of Public Information on the Stock Market," Journal of Finance, American Finance Association, vol. 49(3), pages 923-50, July.
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Cited by:
  1. James D. Hamilton & Oscar Jorda, 2000. "A Model for the Federal Funds Rate Target," NBER Working Papers 7847, National Bureau of Economic Research, Inc.
  2. Rodrigo Herrera & Bernhard Schipp, 2011. "Extreme value models in a conditional duration intensity framework," SFB 649 Discussion Papers SFB649DP2011-022, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  3. Christensen, T.M. & Hurn, A.S. & Lindsay, K.A., 2012. "Forecasting spikes in electricity prices," International Journal of Forecasting, Elsevier, vol. 28(2), pages 400-411.
  4. Fernandes, Marcelo & Grammig, Joachim, 2002. "A Family of Autoregressive Conditional Duration Models," Economics Working Papers (Ensaios Economicos da EPGE) 440, FGV/EPGE Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil).
  5. Wing Lon Ng, 2010. "Dynamic Order Submission And Herding Behavior In Electronic Trading," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(1), pages 27-43.
  6. Zhang Zongxin & Zhang Xiao, 2011. "Trading duration, mutual funds behavior and stock market shock: Based on ACD model to mine mutual funds investment behavior," China Finance Review International, Emerald Group Publishing, vol. 1(3), pages 220-240, June.
  7. Allen, David & Ng, K.H. & Peiris, Shelton, 2013. "The efficient modelling of high frequency transaction data: A new application of estimating functions in financial economics," Economics Letters, Elsevier, vol. 120(1), pages 117-122.
  8. 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, 09.

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