A comparison of financial duration models via density forecasts

Using density forecasts, we compare the predictive performance of duration models that have been developed for modelling intra-day data on stock markets. 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 conditional 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 durations, but fail to do so for trade durations. The evidence is mixed for price durations and ACDbased models, poor for the SCDmo del. The SVDmo del in its original version performs worse than the (Log-)ACDmo dels on the dynamicsof 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.(This abstract was borrowed from another version of this item.)