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A comparison of financial duration models via density forecasts

  • Bauwens, Luc
  • Giot, Pierre
  • Grammig, Joachim
  • Veredas, David

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.

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Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 20 (2004)
Issue (Month): 4 ()
Pages: 589-609

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Handle: RePEc:eee:intfor:v:20:y:2004:i:4:p:589-609
Contact details of provider: Web page: http://www.elsevier.com/locate/ijforecast

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  1. Francis X. Diebold & Todd A. Gunther & Anthony S. Tay, 1997. "Evaluating density forecasts," Working Papers 97-6, Federal Reserve Bank of Philadelphia.
  2. J. Grammig & K. Maurer, 1999. "Non-Monotonic Hazard Functions and the Autoregressive Conditional Duration Model," SFB 373 Discussion Papers 1999,50, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  3. GHYSELS, Eric & HARVEY, Andrew & RENAULT, Eric, 1995. "Stochastic Volatility," CORE Discussion Papers 1995069, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  4. Olivier Renault & Jean-Luc Prigent & Olivier Scaillet, 2000. "An Autoregressive Conditional Binomial Option Pricing Model," FMG Discussion Papers dp364, Financial Markets Group.
  5. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 465-74, October.
  6. VEREDAS, David & RODRIGUEZ-POO, Juan & ESPASA, Antoni, 2002. "On the (intradaily) seasonality and dynamics of a financial point process: a semiparametric approach," CORE Discussion Papers 2002023, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  7. Ghysels, Eric & Gourieroux, Christian & Jasiak, Joann, 2004. "Stochastic volatility duration models," Journal of Econometrics, Elsevier, vol. 119(2), pages 413-433, April.
  8. BAUWENS, Luc & GIOT, Pierre, . "The logarithmic ACD model: an application to the bid-ask quote process of three NYSE stocks," CORE Discussion Papers RP -1497, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  9. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1996. "Stochastic Volatility: Likelihood Inference And Comparison With Arch Models," Econometrics 9610002, EconWPA.
  10. Rombouts, Jeroen V. K. & Bauwens, Luc, 2004. "Econometrics," Papers 2004,33, Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE).
  11. Christian Gourieroux & Joann Jasiak, 2001. "Dynamic Factor Models," Econometric Reviews, Taylor & Francis Journals, vol. 20(4), pages 385-424.
  12. Joachim Grammig & Kai-Oliver Maurer, 2000. "Non-monotonic hazard functions and the autoregressive conditional duration model," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 16-38.
  13. Fernandes, M. & Grammig, J., 2000. "Non-Parametric Specification Tests for Conditional Duration Models," Economics Working Papers eco2000/4, European University Institute.
  14. GIOT, Pierre, 1999. "Time transformations, intraday data and volatility models," CORE Discussion Papers 1999044, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  15. Engle, Robert F. & Russell, Jeffrey R., 1997. "Forecasting the frequency of changes in quoted foreign exchange prices with the autoregressive conditional duration model," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 187-212, June.
  16. Song Chen, 2000. "Probability Density Function Estimation Using Gamma Kernels," Annals of the Institute of Statistical Mathematics, Springer, vol. 52(3), pages 471-480, September.
  17. Granger, C.W.J. & Pesaran, H., 1996. "A Decision_Theoretic Approach to Forecast Evaluation," Cambridge Working Papers in Economics 9618, Faculty of Economics, University of Cambridge.
  18. repec:fth:inseep:9908 is not listed on IDEAS
  19. Chen, Song Xi, 1999. "Beta kernel estimators for density functions," Computational Statistics & Data Analysis, Elsevier, vol. 31(2), pages 131-145, August.
  20. Luc Bauwens & David Veredas, 2004. "The stochastic conditional duration model: a latent factor model for the analysis of financial durations," ULB Institutional Repository 2013/136234, ULB -- Universite Libre de Bruxelles.
  21. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
  22. Easley, David, et al, 1996. " Liquidity, Information, and Infrequently Traded Stocks," Journal of Finance, American Finance Association, vol. 51(4), pages 1405-36, September.
  23. Gouriéroux, Christian & Jasiak, Joanna & Le Fol, Gaëlle, 1999. "Intra-day market activity," Economics Papers from University Paris Dauphine 123456789/5478, Paris Dauphine University.
  24. Madhavan, Ananth, 2000. "Market microstructure: A survey," Journal of Financial Markets, Elsevier, vol. 3(3), pages 205-258, August.
  25. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-83, November.
  26. Zhang, Michael Yuanjie & Russell, Jeffrey R. & Tsay, Ruey S., 2001. "A nonlinear autoregressive conditional duration model with applications to financial transaction data," Journal of Econometrics, Elsevier, vol. 104(1), pages 179-207, August.
  27. repec:fth:inseep:9633 is not listed on IDEAS
  28. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
  29. Goodhart, Charles A. E. & O'Hara, Maureen, 1997. "High frequency data in financial markets: Issues and applications," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 73-114, June.
  30. Feike C. Drost & Bas J. M. Werker, 2000. "Efficient Estimation in Semiparametric Time Series: the ACD Model," Econometric Society World Congress 2000 Contributed Papers 0836, Econometric Society.
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