A Comparison of Financial Duration Models via Density Forecasts
Luc Bauwens (),
Pierre Giot (),
Joachim Grammig () and
David Veredas ()
No 810, Econometric Society World Congress 2000 Contributed Papers from Econometric Society
Using density forecasts, we compare the predictive performance of duration models that have been developed for modelling intra-day data on stock markets. The compared models are the autoregressive conditional duration (ACD) models, their logarithmic versions, in each case with three distributions (Burr, Weibull, and exponential), and the stochastic volatility duration (SVD) model. A pilot Monte Carlo study is conducted to illustrate the relevance of the approach. The evaluation is done on transaction, price, and volume durations of 4 stocks listed at the NYSE. The results lead us to conclude that ACD and Log-ACD models often capture the dependence in the data in a satisfactory way, that they fit correctly the distribution of volume durations, that they fail to do so for trade durations, while the evidence is mixed for price durations.
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (29) Track citations by RSS feed
Downloads: (external link)
http://fmwww.bc.edu/RePEc/es2000/0810.pdf main text (application/pdf)
Journal Article: A comparison of financial duration models via density forecasts (2004)
Working Paper: A comparison of financial duration models via density forecasts (2004)
Working Paper: A comparison of financial duration models via density forecast (2004)
Working Paper: A comparison of financial duration models via density forecasts (2000)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ecm:wc2000:0810
Access Statistics for this paper
More papers in Econometric Society World Congress 2000 Contributed Papers from Econometric Society Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum ().