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Stochastic Conditional Duration Models with Mixture Processes

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Author Info

  • Tony S. Wirjanto

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

  • Adam W. Kolkiewicz

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

  • Zhongxian Men

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

Abstract

This paper studies a stochastic conditional duration (SCD) model with a mixture of distribution processes for financial asset’s transaction data. Specifically it imposes a mixture of two positive distributions on the innovations of the observed duration process, where the mixture component distributions could be either Exponential, Gamma or Weibull. The model also allows for correlation between the observed durations and the logarithm of the latent conditionally expected durations in order to capture a leverage effect known to exist in the equity market. In addition the proposed mixture SCD model is shown to be able to accommodate possibly heavy tails of the marginal distribution of durations. Novel Markov Chain Monte Carlo (MCMC) algorithms are developed for Bayesian inference of parameters and duration forecasting of these models. Simulation studies and empirical applications to two stock duration data sets are provided to assess the performance of the proposed mixture SCD models and the accompanying MCMC algorithms.

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File URL: http://www.rcfea.org/RePEc/pdf/wp29_13.pdf
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Bibliographic Info

Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 29_13.

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Date of creation: May 2013
Date of revision:
Handle: RePEc:rim:rimwps:29_13

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Related research

Keywords: Stochastic conditional duration; Mixture of distributions; Bayesian inference; Markov Chain Monte Carlo; Leverage effect; Slice sampler;

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References

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  1. Bauwens, L. & Lubrano, M., . "Bayesian inference on GARCH models using the Gibbs sampler," CORE Discussion Papers RP -1307, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. 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.
  3. 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.
  4. 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.
  5. Giovanni Luca & Giampiero Gallo, 2009. "Time-Varying Mixing Weights in Mixture Autoregressive Conditional Duration Models," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 102-120.
  6. BAUWENS, Luc & GALLI, Fausto, 2007. "Efficient importance sampling for ML estimation of SCD models," CORE Discussion Papers 2007053, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  7. 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.
  8. Dinghai Xu & Tony S. Wirjanto, 2008. "An Empirical Characteristic Function Approach to VaR under a Mixture of Normal Distribution with Time-Varying Volatility," Working Papers 08008, University of Waterloo, Department of Economics.
  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. 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.
  11. Joachim Grammig & Kai-Oliver Maurer, 2000. "Non-monotonic hazard functions and the autoregressive conditional duration model," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 16-38.
  12. Gareth O. Roberts & Jeffrey S. Rosenthal, 1999. "Convergence of Slice Sampler Markov Chains," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 643-660.
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Cited by:
  1. 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.

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