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Efficient importance sampling for ML estimation of SCD models

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Author Info
Bauwens, L.
Galli, F.

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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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File URL: http://www.sciencedirect.com/science/article/B6V8V-4S1C84Y-2/2/83bdb47eced7fa6c7e80b822c7483576
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Publisher 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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Web page: http://www.elsevier.com/locate/csda

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  1. 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). [Downloadable!]
  2. Dingan Feng, 2004. "Stochastic Conditional Duration Models with "Leverage Effect" for Financial Transaction Data," Journal of Financial Econometrics, Oxford University Press, vol. 2(3), pages 390-421. [Downloadable!] (restricted)
  3. Ghysels, E. & Harvey, A. & Renault, E., 1996. "Stochastic Volatility," Cahiers de recherche 9613, Universite de Montreal, Departement de sciences economiques. [Downloadable!]
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  4. Luc Bauwens & Nikolaus Hautsch, 2006. "Stochastic Conditional Intensity Processes," Journal of Financial Econometrics, Oxford University Press, vol. 4(3), pages 450-493. [Downloadable!] (restricted)
  5. Strickland, Chris M. & Forbes, Catherine S. & Martin, Gael M., 2006. "Bayesian analysis of the stochastic conditional duration model," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2247-2267, May. [Downloadable!] (restricted)
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