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Efficient importance sampling for ML estimation of SCD models

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  • BAUWENS, Luc

    (Université catholique de Louvain (UCL). Center for Operations Research and Econometrics (CORE))

  • GALLI, Fausto

Abstract

The evaluation of the likelihood function of the stochastic conditional duration model requires to compute an integral that has the dimension of the sample size. We apply the efficient importance sampling method for computing this integral. We compare EIS-based ML estimation with QML estimation based on the Kalman filter. We find that EIS-ML estimation is more precise statistically, at a cost of an acceptable loss of quickness of computations. We illustrate this with simulated and real data. We show also that the EIS-ML method is easy to apply to extensions of the SCD model.

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File URL: http://www.uclouvain.be/cps/ucl/doc/core/documents/coredp2007_53.pdf
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Bibliographic Info

Paper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 2007053.

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Date of creation: 01 Aug 2007
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Handle: RePEc:cor:louvco:2007053

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Keywords: stochastic conditional duration; importance sampling;

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References

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  1. GHYSELS, Eric & HARVEY, Andrew & RENAULT, Eric, 1995. "Stochastic Volatility," CORE Discussion Papers 1995069, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. Liesenfeld, Roman & Richard, Jean-François, 2006. "Improving MCMC Using Efficient Importance Sampling," Economics Working Papers 2006,05, Christian-Albrechts-University of Kiel, Department of Economics.
  3. BAUWENS, Luc & ROMBOUTS, Jeroen V.K., . "Econometrics," CORE Discussion Papers RP -1713, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Rombouts, Jeroen V. K. & Bauwens, Luc, 2004. "Econometrics," Papers 2004,33, Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE).
  4. Dingan Feng, 2004. "Stochastic Conditional Duration Models with "Leverage Effect" for Financial Transaction Data," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(3), pages 390-421.
  5. 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).
  6. Jung, Robert C. & Kukuk, Martin & Liesenfeld, Roman, 2006. "Time series of count data: modeling, estimation and diagnostics," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2350-2364, December.
  7. Jean-Francois Richard, 2007. "Efficient High-Dimensional Importance Sampling," Working Papers 321, University of Pittsburgh, Department of Economics, revised Jan 2007.
  8. BAUWENS, Luc & HAUTSCH, Nikolaus, . "Stochastic conditional intensity processes," CORE Discussion Papers RP -1937, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  9. Chris M. Strickland & Catherine S. Forbes & Gael M. Martin, 2003. "Bayesian Analysis of the Stochastic Conditional Duration Model," Monash Econometrics and Business Statistics Working Papers 14/03, Monash University, Department of Econometrics and Business Statistics.
  10. Liesenfeld, Roman & Richard, Jean-Francois, 2003. "Univariate and multivariate stochastic volatility models: estimation and diagnostics," Journal of Empirical Finance, Elsevier, vol. 10(4), pages 505-531, September.
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Citations

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Cited by:
  1. Fok, D. & Paap, R. & Franses, Ph.H.B.F., 2002. "Modeling dynamic effects of promotion on interpurchase times," Econometric Institute Research Papers EI 2002-37, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  2. Pastorello, S. & Rossi, E., 2010. "Efficient importance sampling maximum likelihood estimation of stochastic differential equations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2753-2762, November.
  3. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54841, University Library of Munich, Germany.
  4. Skaug, Hans J. & Yu, Jun, 2014. "A flexible and automated likelihood based framework for inference in stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 642-654.
  5. Kleppe, Tore Selland & Liesenfeld, Roman, 2011. "Efficient high-dimensional importance sampling in mixture frameworks," Economics Working Papers 2011,11, Christian-Albrechts-University of Kiel, Department of Economics.
  6. Tore Selland KLEPPE & Jun YU & Hans J. SKAUG, 2009. "Stimulated Maximum Likelihood Estimation of Continuous Time Stochastic Volatility Models," Working Papers 20-2009, Singapore Management University, School of Economics.
  7. Maria Pacurar, 2008. "Autoregressive Conditional Duration Models In Finance: A Survey Of The Theoretical And Empirical Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 22(4), pages 711-751, 09.
  8. Tony S. Wirjanto & Adam W. Kolkiewicz & Zhongxian Men, 2013. "Stochastic Conditional Duration Models with Mixture Processes," Working Paper Series 29_13, The Rimini Centre for Economic Analysis.
  9. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54030, University Library of Munich, Germany.
  10. Andre A. Monteiro, 2009. "The econometrics of randomly spaced financial data: a survey," Statistics and Econometrics Working Papers ws097924, Universidad Carlos III, Departamento de Estadística y Econometría.
  11. Zhongxian Men & Tony S. Wirjanto & Adam W. Kolkiewicz, 2013. "Bayesian Inference of Multiscale Stochastic Conditional Duration Models," Working Paper Series 63_13, The Rimini Centre for Economic Analysis.
  12. Zhongxian Men & Adam W. Kolkiewicz & Tony S. Wirjanto, 2013. "Bayesian Inference of Asymmetric Stochastic Conditional Duration Models," Working Paper Series 28_13, The Rimini Centre for Economic Analysis.

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