期刊名称：CORE Discussion Papers / Center for Operations Research and Econometrics (UCL), Louvain
出版年度：2000
卷号：2000
出版社：Center for Operations Research and Econometrics (UCL), Louvain
摘要：Using density forecasts, we compare the predictive performance of duration
models that have been developed for modelling intra-day data on stock mar-
kets. Our model portfolio encompasses the autoregressive conditional duration
(ACD) model, its logarithmic version (Log-ACD), the threshold ACD (TACD)
model – in each case with alternative error distributions –, the stochastic con-
ditional duration model (SCD), and the stochastic volatility duration model
(SVD). The evaluation is done on transaction, price, and volume durations of
four stocks listed at the NYSE. The results lead us to conclude that the ACD/log-
ACD/TACD/SCD models capture the dynamic dependence in the data in a
satisfactory way. They fit correctly the conditional distribution of volume dur-
ations, but fail to do so for trade durations. The evidence is mixed for price
durations and ACDbased models, poor for the SCDmodel. The SVDmodel in
its original version performs worse than the (Log-)ACDmodels on the dynamics
of trade durations, and offers no improvement with respect to the distributional
aspect. The SVDis not suitable to model volume durations. Regarding price
durations the performance of the SVDis comparable to those of (Log-)ACD
specifications that provide the best results.
关键词：duration, high frequency data, density forecast.
