Time-varying autoregressive conditional duration model |
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Authors: | Adriana B Bortoluzzo Pedro A Morettin Clelia MC Toloi |
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Institution: | 1. Department of Statistics, Ibmec S?o Paulo , Rua Quata 300, Sao Paulo , 04546042 , Brazil;2. IME-USP , S?o Paulo , Brazil |
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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 the time-varying ACD model simultaneously modeled the dependence between durations, intra-day seasonality and volatility. |
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Keywords: | ACD model bootstrap durations non-stationarity time-varying parameters wavelet |
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