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Efficient importance sampling for ML estimation of SCD models

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  • Bauwens, L.
  • Galli, F.

Abstract

The evaluation of the likelihood function of the stochastic conditional duration (SCD) model requires to compute an integral that has the dimension of the sample size. ML estimation based on the efficient importance sampling (EIS) method is developed for computing this integral and compared with QML estimation based on the Kalman filter. Based on Monte Carlo experiments, EIS-ML estimation is found to be more precise statistically, but involves an acceptable loss of quickness of computations. The method is illustrated with real data and is shown to be easily applicable to extensions of the SCD model.

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Bibliographic Info

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 53 (2009)
Issue (Month): 6 (April)
Pages: 1974-1992

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Handle: RePEc:eee:csdana:v:53:y:2009:i:6:p:1974-1992

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  1. 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.
  2. Rombouts, Jeroen V. K. & Bauwens, Luc, 2004. "Econometrics," Papers 2004,33, Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE).
  3. Liesenfeld, Roman & Richard, Jean-Francois, 2003. "Univariate and multivariate stochastic volatility models: estimation and diagnostics," Journal of Empirical Finance, Elsevier, Elsevier, vol. 10(4), pages 505-531, September.
  4. BAUWENS, Luc & HAUTSCH, Nikolaus, . "Stochastic conditional intensity processes," CORE Discussion Papers RP -1937, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  5. Strickland, Chris M. & Forbes, Catherine S. & Martin, Gael M., 2006. "Bayesian analysis of the stochastic conditional duration model," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 50(9), pages 2247-2267, May.
  6. Ghysels, E. & Harvey, A. & Renault, E., 1996. "Stochastic Volatility," Cahiers de recherche, Universite de Montreal, Departement de sciences economiques 9613, Universite de Montreal, Departement de sciences economiques.
  7. Jung, Robert C. & Kukuk, Martin & Liesenfeld, Roman, 2006. "Time series of count data: modeling, estimation and diagnostics," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 51(4), pages 2350-2364, December.
  8. Richard, Jean-Francois & Zhang, Wei, 2007. "Efficient high-dimensional importance sampling," Journal of Econometrics, Elsevier, Elsevier, vol. 141(2), pages 1385-1411, December.
  9. 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.
  10. BAUWENS, Luc & VEREDAS, David, 1999. "The stochastic conditional duration model: a latent factor model for the analysis of financial durations," CORE Discussion Papers, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) 1999058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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Cited by:
  1. Andre A. Monteiro, 2009. "The econometrics of randomly spaced financial data: a survey," Statistics and Econometrics Working Papers, Universidad Carlos III, Departamento de Estadística y Econometría ws097924, Universidad Carlos III, Departamento de Estadística y Econometría.
  2. 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.
  3. 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.
  4. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54030, University Library of Munich, Germany.
  5. Tony S. Wirjanto & Adam W. Kolkiewicz & Zhongxian Men, 2013. "Stochastic Conditional Duration Models with Mixture Processes," Working Paper Series, The Rimini Centre for Economic Analysis 29_13, The Rimini Centre for Economic Analysis.
  6. Zhongxian Men & Adam W. Kolkiewicz & Tony S. Wirjanto, 2013. "Bayesian Inference of Asymmetric Stochastic Conditional Duration Models," Working Paper Series, The Rimini Centre for Economic Analysis 28_13, The Rimini Centre for Economic Analysis.
  7. Zhongxian Men & Tony S. Wirjanto & Adam W. Kolkiewicz, 2013. "Bayesian Inference of Multiscale Stochastic Conditional Duration Models," Working Paper Series, The Rimini Centre for Economic Analysis 63_13, The Rimini Centre for Economic Analysis.
  8. 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.
  9. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54841, University Library of Munich, Germany.
  10. Pastorello, S. & Rossi, E., 2010. "Efficient importance sampling maximum likelihood estimation of stochastic differential equations," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 54(11), pages 2753-2762, November.
  11. 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.
  12. Skaug, Hans J. & Yu, Jun, 2014. "A flexible and automated likelihood based framework for inference in stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 76(C), pages 642-654.

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