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Generalized Moment Tests for Autoregressive Conditional Duration Models

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  • Yi-Ting Chen

Abstract

Autoregressive conditional duration (ACD) models have been shown to be important for several applications in empirical finance. In this paper, we consider a set of generalized moment tests for the conditional mean specifications, the IIDness assumption of the error terms, and the distribution assumptions of the error terms in the context of ACD models. These generalized tests are also applicable to other multiplicative error models. We demonstrate that these tests are useful for unifying existing parametric tests, correcting the estimation effect ignored by some popular tests, and generating new tests for ACD models. Therefore, they can be applied to evaluate ACD models in a more complete way. This study also includes a Monte Carlo simulation and an empirical example to assess the performance of these tests. (JEL: C52, G15) Copyright The Author 2010. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org, Oxford University Press.

Suggested Citation

  • Yi-Ting Chen, 2010. "Generalized Moment Tests for Autoregressive Conditional Duration Models," Journal of Financial Econometrics, Oxford University Press, vol. 8(3), pages 345-391, Summer.
    • Handle: RePEc:oup:jfinec:v:8:y:2010:i:3:p:345-391
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbq016
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    Cited by:

    1. Ng, F.C. & Li, W.K. & Yu, Philip L.H., 2016. "Diagnostic checking of the vector multiplicative error model," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 86-97.
    2. Hira L. Koul & Indeewara Perera & Narayana Balakrishna, 2023. "A class of Minimum Distance Estimators in Markovian Multiplicative Error Models," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 87-115, May.
    3. Perera, Indeewara & Koul, Hira L., 2017. "Fitting a two phase threshold multiplicative error model," Journal of Econometrics, Elsevier, vol. 197(2), pages 348-367.
    4. Guo, Bin & Li, Shuo, 2018. "Diagnostic checking of Markov multiplicative error models," Economics Letters, Elsevier, vol. 170(C), pages 139-142.
    5. Ke, Rui & Lu, Wanbo & Jia, Jing, 2021. "Evaluating multiplicative error models: A residual-based approach," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).

    More about this item

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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