Please use this identifier to cite or link to this item: https://repositorio.insper.edu.br/handle/11224/5758
Type: Working Paper
Title: Time-Varying Autoregressive Conditional Duration Model
Author: Bortoluzzo, Adriana Bruscato
Morettin, Pedro A.
Toloi, Clelia M. C.
Publication Date: 2009
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.
Keywords (english terms): ACD model
bootstrap
durations
non-stationarity
time-varying parameters
wavelet
Language: Inglês
CNPq Area: Ciências Exatas e da Terra
Copyright: O INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DO USUÁRIO VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITOR
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