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Forecasting using high-frequency data: a comparison of asymmetric financial duration models

Author

Listed:
  • Qi Zhang

    (Leeds University Business School, UK)

  • Charlie X Cai

    (Leeds University Business School, UK)

  • Kevin Keasey

    (Leeds University Business School, UK)

Abstract

The first purpose of this paper is to assess the short-run forecasting capabilities of two competing financial duration models. The forecast performance of the Autoregressive Conditional Multinomial-Autoregressive Conditional Duration (ACM-ACD) model is better than the Asymmetric Autoregressive Conditional Duration (AACD) model. However, the ACM-ACD model is more complex in terms of the computational setting and is more sensitive to starting values. The second purpose is to examine the effects of market microstructure on the forecasting performance of the two models. The results indicate that the forecast performance of the models generally decreases as the liquidity of the stock increases, with the exception of the most liquid stocks. Furthermore, a simple filter of the raw data improves the performance of both models. Finally, the results suggest that both models capture the characteristics of the micro data very well with a minimum sample length of 20 days. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • Qi Zhang & Charlie X Cai & Kevin Keasey, 2009. "Forecasting using high-frequency data: a comparison of asymmetric financial duration models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(5), pages 371-386.
    • Handle: RePEc:jof:jforec:v:28:y:2009:i:5:p:371-386
    DOI: 10.1002/for.1100
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    References listed on IDEAS

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    Cited by:

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    2. Erhard Reschenhofer & Manveer Kaur Mangat & Christian Zwatz & Sándor Guzmics, 2020. "Evaluation of current research on stock return predictability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 334-351, March.

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