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Time-Varying Autoregressive Conditional Duration Model

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  • Adriana Bruscato Bortoluzzo
  • Pedro A. Morettin
  • Clelia M. C. Toloi

Abstract

The main goal of this work is to generalize the autoregressive conditional duration (ACD) model applied to times between trades to the case of time-varying parameters. The use of wavelets allows that parameters vary through time and makes possible the modeling of non-stationary processes without preliminary data transformations. The time-varying ACD model estimation was done by maximum likelihood with standard exponential distributed errors. The properties of the estimators were assessed via bootstrap. We present a simulation exercise for a non-stationary process and an empirical application to a real series, namely the TELEMAR stock. Diagnostic and goodness of fit analysis suggest that time-varying ACD model simultaneously modelled the dependence between durations, intra-day seasonality and volatility.

Suggested Citation

  • Adriana Bruscato Bortoluzzo & Pedro A. Morettin & Clelia M. C. Toloi, 2009. "Time-Varying Autoregressive Conditional Duration Model," Business and Economics Working Papers 059, Unidade de Negocios e Economia, Insper.
    • Handle: RePEc:aap:wpaper:059
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    References listed on IDEAS

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