Time-Varying Autoregressive Conditional Duration Model
Código: WPE – 174
Adriana B. Bortoluzzo
Pedro A. Morettin
Clelia M. C. Toloi
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.