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The stochastic conditional duration model: a latent factor model for the analysis of financial durations

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Author Info
BAUWENS, Luc
VEREDAS, David

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Abstract

A new model for the analysis of durations, the stochastic conditional duration (SCD) model, is introduced. This model is based of the assumption that the durations are generated by a latent stochastic factor that follows a first order autoregressive process. The latent factor is pertubed multiplicatively by an innovation distributed as aWeibull or gamma variable. The model can capture a wide range of shapes of hazard functions. The estimation of the parameters is performed by quasi-maximum likelihood, after transforming the original nonlinear model into a space state representation and using the Kalman filter. The model is applied to stock market price-durations, looking at the relation between price durations, volume, spread and trading intensity.

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Publisher Info
Paper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 1999058.

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Date of creation: 01 Nov 1999
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Handle: RePEc:cor:louvco:1999058

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Related research
Keywords: Duration; High frequency data; Market microstucture; Factor model.;

Find related papers by JEL classification:
C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - General
C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis
G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

References listed on IDEAS
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  1. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-55, January. [Downloadable!] (restricted)
  2. Robert F. Engle, 1996. "The Econometrics of Ultra-High Frequency Data," NBER Working Papers 5816, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  3. Luc Bauwens & Pierre Giot, 2003. "Asymmetric ACD models: Introducing price information in ACD models," Empirical Economics, Springer, vol. 28(4), pages 709-731, November. [Downloadable!] (restricted)
  4. Easley, David & O'Hara, Maureen, 1992. " Time and the Process of Security Price Adjustment," Journal of Finance, American Finance Association, vol. 47(2), pages 576-605, June.
  5. Jeffrey R. Russell & Robert F. Engle, 1998. "Econometric Analysis of Discrete-Valued Irregularly-Spaced Financial Transactions Data Using a New Autoregressive Conditional Multinomial Model," University of California at San Diego, Economics Working Paper Series 98-10, Department of Economics, UC San Diego. [Downloadable!]
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  6. Ruiz, Esther, 1994. "Quasi-maximum likelihood estimation of stochastic volatility models," Journal of Econometrics, Elsevier, vol. 63(1), pages 289-306, July. [Downloadable!] (restricted)
  7. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 1994. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 371-89, October.
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  8. Harvey, Andrew & Ruiz, Esther & Shephard, Neil, 1994. "Multivariate Stochastic Variance Models," Review of Economic Studies, Blackwell Publishing, vol. 61(2), pages 247-64, April. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Nikolaus Hautsch, 2007. "Capturing Common Components in High-Frequency Financial Time Series: A Multivariate Stochastic Multiplicative Error Model," CFS Working Paper Series 2007/25, Center for Financial Studies. [Downloadable!]
    Other versions:
  2. Taoufik Bouezmarni & Jeroen V.K. Rombouts, 2006. "Nonparametric Density Estimation for Positive Time Series," Cahiers de recherche 06-09, HEC Montréal, Institut d'économie appliquée. [Downloadable!]
    Other versions:
  3. repec:bep:sndecm:11:2007:2:1377-1377 is not listed on IDEAS
  4. BAUWENS, Luc & HAUTSCH, Nikolaus, 2003. "Dynamic latent factor models for intensity processes," CORE Discussion Papers 2003103, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE). [Downloadable!]
  5. Fernandes, Marcelo & Grammig, Joachim, 2002. "A Family of Autoregressive Conditional Duration Models," Economics Working Papers (Ensaios Economicos da EPGE) 440, Graduate School of Economics, Getulio Vargas Foundation (Brazil). [Downloadable!]
    Other versions:
  6. PREMINGER, Arie & HAFNER, Christian M., 2006. "Deciding between GARCH and stochastic volatility via strong decision rules," CORE Discussion Papers 2006042, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE). [Downloadable!]
  7. Frank Gerhard & Nikolaus Hautsch, 2006. "A Dynamic Semiparametric Proportional Hazard Model," FRU Working Papers 2006/05, University of Copenhagen. Department of Economics. Finance Research Unit. [Downloadable!]
  8. Luc, BAUWENS & Nikolaus, HAUTSCH, 2006. "Modelling Financial High Frequency Data Using Point Processes," Discussion Papers (ECON - Département des Sciences Economiques) 2006039, Université catholique de Louvain, Département des Sciences Economiques. [Downloadable!]
    Other versions:
  9. Fernandes, Marcelo & Grammig, Joachim, 2003. "Nonparametric specification tests for conditional duration models," Economics Working Papers (Ensaios Economicos da EPGE) 502, Graduate School of Economics, Getulio Vargas Foundation (Brazil). [Downloadable!]
    Other versions:
  10. Peter X.-K. Song & Yanqin Fan & John D. Kalbfleisch, 2003. "Maximization by Parts in Likelihood Inference," Working Papers 0319, Department of Economics, Vanderbilt University. [Downloadable!]
  11. Nikolaus Hautsch, 2005. "The latent factor VAR model: Testing for a common component in the intraday trading process," FRU Working Papers 2005/03, University of Copenhagen. Department of Economics. Finance Research Unit. [Downloadable!]
  12. David Veredas & Juan M. Rodríguez-Poo & Antoni Espasa, 2001. "On The (Intradaily) Seasonality And Dynamics Of A Financial Point Process: A Semiparametric Approach," Statistics and Econometrics Working Papers ws013321, Universidad Carlos III, Departamento de Estadística y Econometría. [Downloadable!]
    Other versions:
  13. 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. [Downloadable!]
    Other versions:
  14. Nikolaus Hautsch & Winfried Pohlmeier, 2001. "Econometric Analysis of Financial Transaction Data: Pitfalls and Opportunities," CoFE Discussion Paper 01-05, Center of Finance and Econometrics, University of Konstanz. [Downloadable!]
  15. Anthony D. Hall & Nikolaus Hautsch, 2004. "Order Aggressiveness and Order Book Dynamics," FRU Working Papers 2005/04, University of Copenhagen. Department of Economics. Finance Research Unit. [Downloadable!]
    Other versions:
  16. repec:bep:sndecm:11:2007:2:1373-1373 is not listed on IDEAS
  17. André A. Monteiro, 2008. "Parameter Driven Multi-state Duration Models: Simulated vs. Approximate Maximum Likelihood Estimation," Tinbergen Institute Discussion Papers 08-021/2, Tinbergen Institute. [Downloadable!]
  18. Giovanni De Luca & Giampiero Gallo, 2006. "Time-varying Mixing Weights in Mixture Autoregressive Conditional Duration Models," Econometrics Working Papers Archive wp2006_12, Universita' degli Studi di Firenze, Dipartimento di Statistica "G. Parenti". [Downloadable!]
    Other versions:
  19. Luc, BAUWENS & Fausto Galli, 2007. "Efficient importance sampling for ML estimation of SCD models," Discussion Papers (ECON - Département des Sciences Economiques) 2007032, Université catholique de Louvain, Département des Sciences Economiques. [Downloadable!]
    Other versions:
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