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Stochastic Conditional Duration Models with "Leverage Effect" for Financial Transaction Data

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  • Dingan Feng
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    Abstract

    This article proposes stochastic conditional duration (SCD) models with "leverage effect" for financial transaction data, which extends both the autoregressive conditional duration (ACD) model (Engle and Russell, 1998, Econometrica, 66, 1127--1162) and the existing SCD model (Bauwens and Veredas, 2004, Journal of Econometrics, 119, 381--412). The proposed models belong to a class of linear nongaussian state-space models, where the observation equation for the duration process takes an additive form of a latent process and a noise term. The latent process is driven by an autoregressive component to characterize the transition property and a term associated with the observed duration. The inclusion of such a term allows the model to capture the asymmetric behavior or "leverage effect" of the expected duration. The Monte Carlo maximum-likelihood (MCML) method is employed for consistent and efficient parameter estimation with applications to the transaction data of IBM and other stocks. Our analysis suggests that trade intensity is correlated with stock return volatility and modeling the duration process with "leverage effect" can enhance the forecasting performance of intraday volatility. Copyright 2004, Oxford University Press.

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    Bibliographic Info

    Article provided by Society for Financial Econometrics in its journal Journal of Financial Econometrics.

    Volume (Year): 2 (2004)
    Issue (Month): 3 ()
    Pages: 390-421

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    Handle: RePEc:oup:jfinec:v:2:y:2004:i:3:p:390-421

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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.
    2. BAUWENS, Luc & GALLI, Fausto, . "Efficient importance sampling for ML estimation of SCD models," CORE Discussion Papers RP -2088, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Dingan Feng & Peter X.-K. Song & Tony S. Wirjanto, 2008. "Time-Deformation Modeling Of Stock Returns Directed By Duration Processes," Working Papers 08010, University of Waterloo, Department of Economics.
    4. 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.
    5. Christensen, T.M. & Hurn, A.S. & Lindsay, K.A., 2012. "Forecasting spikes in electricity prices," International Journal of Forecasting, Elsevier, vol. 28(2), pages 400-411.
    6. 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.

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