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A Comparison of Financial Duration Models via Density Forecasts

Author

Listed:
  • Luc Bauwens

    (Universite Catholique de Louvain)

  • Pierre Giot

    (Universite Catholique de Louvain)

  • Joachim Grammig

    (University of Frankfurt)

  • David Veredas

    (Universite Catholique de Louvain)

Abstract

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.

Suggested Citation

  • Luc Bauwens & Pierre Giot & Joachim Grammig & David Veredas, 2000. "A Comparison of Financial Duration Models via Density Forecasts," Econometric Society World Congress 2000 Contributed Papers 0810, Econometric Society.
  • Handle: RePEc:ecm:wc2000:0810
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    References listed on IDEAS

    as
    1. Francis X. Diebold & Todd A. Gunther & Anthony S. Tay, "undated". "Evaluating Density Forecasts," CARESS Working Papres 97-18, University of Pennsylvania Center for Analytic Research and Economics in the Social Sciences.
    2. BAUWENS, Luc & VEREDAS, David, 1999. "The stochastic conditional duration model: a latent factor model for the analysis of financial durations," CORE Discussion Papers 1999058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. 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.
    4. Christian Gourieroux & Joann Jasiak, 2001. "Dynamic Factor Models," Econometric Reviews, Taylor & Francis Journals, vol. 20(4), pages 385-424.
    5. Madhavan, Ananth, 2000. "Market microstructure: A survey," Journal of Financial Markets, Elsevier, vol. 3(3), pages 205-258, August.
    6. 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.
    7. Jean -Luc Prigent & Olivier Renault & Olivier Scaillet, 1999. "An Autoregressive Conditional Binomial Option Pricing Model," Working Papers 99-65, Center for Research in Economics and Statistics.
    8. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 465-474, October.
    9. Eric Ghysels & Andrew Harvey & Éric Renault, 1995. "Stochastic Volatility," CIRANO Working Papers 95s-49, CIRANO.
    10. David Veredas & Juan Rodriguez-Poo & Antoni Espasa, 2001. "On the (Intradaily) Seasonality and Dynamics of a Financial Point Process : A Semiparametric Approach," Working Papers 2001-19, Center for Research in Economics and Statistics.
    11. Gourieroux, Christian & Jasiak, Joanna & Le Fol, Gaelle, 1999. "Intra-day market activity," Journal of Financial Markets, Elsevier, vol. 2(3), pages 193-226, August.
    12. 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.
    13. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 361-393.
    14. Fernandes, Marcelo & Grammig, Joachim, 2005. "Nonparametric specification tests for conditional duration models," Journal of Econometrics, Elsevier, vol. 127(1), pages 35-68, July.
    15. Ghysels, Eric & Gourieroux, Christian & Jasiak, Joann, 2004. "Stochastic volatility duration models," Journal of Econometrics, Elsevier, vol. 119(2), pages 413-433, April.
    16. 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.
    17. repec:dau:papers:123456789/5478 is not listed on IDEAS
    18. 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.
    19. 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.
    20. 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 863-883, November.
    21. repec:crs:wpaper:9908 is not listed on IDEAS
    22. Easley, David, et al, 1996. " Liquidity, Information, and Infrequently Traded Stocks," Journal of Finance, American Finance Association, vol. 51(4), pages 1405-1436, September.
    23. repec:crs:wpaper:9633 is not listed on IDEAS
    24. 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.
    25. Song Chen, 2000. "Probability Density Function Estimation Using Gamma Kernels," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 52(3), pages 471-480, September.
    26. repec:adr:anecst:y:2000:i:60:p:05 is not listed on IDEAS
    27. Chen, Song Xi, 1999. "Beta kernel estimators for density functions," Computational Statistics & Data Analysis, Elsevier, vol. 31(2), pages 131-145, August.
    28. 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.
    29. 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).
    30. Rombouts, Jeroen V. K. & Bauwens, Luc, 2004. "Econometrics," Papers 2004,33, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
    31. 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.
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    More about this item

    JEL classification:

    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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