IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

Bayesian Inference of Multiscale Stochastic Conditional Duration Models

  • Zhongxian Men

    (Department of Statistics and Actuarial Science, University of Waterloo, Canada)

  • Tony S. Wirjanto

    ()

    (Department of Statistics and Actuarial Science, University of Waterloo, Canada)

  • Adam W. Kolkiewicz

    (Department of Statistics and Actuarial Science, School of Accounting and Finance, University of Waterloo, Canada)

There is evidence to suggest that a single factor of duration running on single time scale is not adequate to capture the dynamics of the duration process of financial transaction data. This assertion is motivated by the observation that some existing one-factor stochastic duration models have had difficulty in successfully fitting the left tail of the marginal distribution of the observed durations. This empirical poor fit of the left tail of the duration distribution may be indicative of the possible existence of multiple stochastic duration factors running on different time scales. This paper proposes multiscale stochastic conditional duration (MSCD) models to describe the dynamics of financial transaction data. Novel algorithms of Markov Chain Monte Carlo (MCMC) are developed to fit the resulting MSCD models under three distributional assumptions about the innovation of the measurement equation. In addition, instead of subjecting the observation equation to a logarithmic transformation, we work on the MSCD model directly. Simulation studies suggest that our proposed models and corresponding estimation methodology work quite well. We also apply an auxiliary particle filter technique to construct one-step-ahead in-sample and out-of-sample duration forecasts based on the fitted models. Applications to two duration data sets of FIAT and IBM indicate the existence of at least two factors that determine the dynamics of the two stock transactions.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.rcfea.org/RePEc/pdf/wp63_13.pdf
Download Restriction: no

Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 63_13.

as
in new window

Length:
Date of creation: Dec 2013
Date of revision:
Handle: RePEc:rim:rimwps:63_13
Contact details of provider: Postal: Via Patara, 3, 47921 Rimini (RN)
Phone: +390541434142
Fax: +39054155431
Web page: http://www.rcfea.org
Email:


More information through EDIRC

References listed on IDEAS
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.:

as in new window
  1. Neil Shephard & Siddhartha Chib, 1999. "Analysis of High Dimensional Multivariate Stochastic Volatility Models," Economics Series Working Papers 1999-W18, University of Oxford, Department of Economics.
  2. Bauwens, L. & Galli, F., 2009. "Efficient importance sampling for ML estimation of SCD models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 1974-1992, April.
  3. Dinghai Xu & John Knight & Tony S. Wirjanto, 2008. "Asymmetric Stochastic Conditional Duration Model --A Mixture of Normals Approach"," Working Papers 08007, University of Waterloo, Department of Economics.
  4. Hirotugu Akaike, 1987. "Factor analysis and AIC," Psychometrika, Springer, vol. 52(3), pages 317-332, September.
  5. BAUWENs, Luc & LUBRANO , Michel, 1996. "Bayesian Inference on GARCH Models using the Gibbs Sampler," CORE Discussion Papers 1996027, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  6. 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.
  7. 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.
  8. Zhongxian Men & Tony S. Wirjanto & Adam W. Kolkiewicz, 2013. "A Threshold Stochastic Conditional Duration Model for Financial Transaction Data," Working Paper Series 30_13, The Rimini Centre for Economic Analysis.
  9. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-83, November.
  10. De Luca Giovanni & Gallo Giampiero M., 2004. "Mixture Processes for Financial Intradaily Durations," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(2), pages 1-20, May.
  11. Berg, Andreas & Meyer, Renate & Yu, Jun, 2004. "Deviance Information Criterion for Comparing Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 107-20, January.
  12. John Knight & Cathy Q. Ning, 2008. "Estimation of the stochastic conditional duration model via alternative methods," Econometrics Journal, Royal Economic Society, vol. 11(3), pages 593-616, November.
  13. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  14. 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.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:rim:rimwps:63_13. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dimitrios Vortelinos)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.