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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.

Suggested Citation

  • BAUWENS, Luc & GALLI, Fausto, 2007. "Efficient importance sampling for ML estimation of SCD models," LIDAM Discussion Papers CORE 2007053, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvco:2007053
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    1. Bauwens, Luc & Veredas, David, 2004. "The stochastic conditional duration model: a latent variable model for the analysis of financial durations," Journal of Econometrics, Elsevier, vol. 119(2), pages 381-412, April.
    2. Liesenfeld, Roman & Richard, Jean-François, 2008. "Improving MCMC, using efficient importance sampling," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 272-288, December.
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    Cited by:

    1. 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.
    2. Zhongxian Men & Tony S. Wirjanto & Adam W. Kolkiewicz, 2016. "A Multiscale Stochastic Conditional Duration Model," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 11(04), pages 1-28, December.
    3. Jean-François Richard, 2015. "Likelihood Evaluation of High-Dimensional Spatial Latent Gaussian Models with Non-Gaussian Response Variables," Working Paper 5778, Department of Economics, University of Pittsburgh.
    4. Tore Selland Kleppe & Jun Yu & H.J. Skaug, 2010. "Simulated maximum likelihood estimation of continuous time stochastic volatility models," Advances in Econometrics, in: Maximum Simulated Likelihood Methods and Applications, pages 137-161, Emerald Group Publishing Limited.
    5. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54030, University Library of Munich, Germany.
    6. 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.
    7. 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.
    8. Zhongxian Men & Tony S. Wirjanto & Adam W. Kolkiewicz, 2013. "Bayesian Inference of Multiscale Stochastic Conditional Duration Models," Working Paper series 63_13, Rimini Centre for Economic Analysis.
    9. Scharth, Marcel & Kohn, Robert, 2016. "Particle efficient importance sampling," Journal of Econometrics, Elsevier, vol. 190(1), pages 133-147.
    10. Fok, Dennis & Paap, Richard & Franses, Philip Hans, 2012. "Modeling dynamic effects of promotion on interpurchase times," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3055-3069.
    11. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54841, University Library of Munich, Germany.
    12. Kleppe, Tore Selland & Liesenfeld, Roman, 2014. "Efficient importance sampling in mixture frameworks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 449-463.
    13. Siem Jan Koopman & André Lucas & Marcel Scharth, 2015. "Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State-Space Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 114-127, January.
    14. Tony S. Wirjanto & Adam W. Kolkiewicz & Zhongxian Men, 2013. "Stochastic Conditional Duration Models with Mixture Processes," Working Paper series 29_13, Rimini Centre for Economic Analysis.
    15. 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, September.
    16. Monteiro, André A., 2009. "The econometrics of randomly spaced financial data: a survey," DES - Working Papers. Statistics and Econometrics. WS ws097924, Universidad Carlos III de Madrid. Departamento de Estadística.
    17. Bekierman Jeremias & Gribisch Bastian, 2016. "Estimating stochastic volatility models using realized measures," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(3), pages 279-300, June.
    18. Zhongxian Men & Adam W. Kolkiewicz & Tony S. Wirjanto, 2013. "Bayesian Inference of Asymmetric Stochastic Conditional Duration Models," Working Paper series 28_13, Rimini Centre for Economic Analysis.

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    More about this item

    Keywords

    stochastic conditional duration; importance sampling;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

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