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Predicting Stock Volatility Using After-Hours Information

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Listed:
  • Chun-Hung Chen

    (KPMG)

  • Wei-Choun Yu

    (Winona State University)

  • Eric Zivot

    (University of Washington)

Abstract

We use realized volatilities based on after hours high frequency returns to predict next day volatility. We extend GARCH and long-memory forecasting models to include additional information: the whole night, the preopen, the postclose realized variance, and the overnight squared return. For four NASDAQ stocks (MSFT, AMGN, CSCO, and YHOO) we find that the inclusion of the preopen variance can improve the out-of-sample forecastability of the next day conditional day volatility. Additionally, we find that the postclose variance and the overnight squared return do not provide any predictive power for the next day conditional volatility. Our findings support the results of prior studies that traders trade for non-information reasons in the postclose period and trade for information reasons in the preopen period.

Suggested Citation

  • Chun-Hung Chen & Wei-Choun Yu & Eric Zivot, 2009. "Predicting Stock Volatility Using After-Hours Information," Working Papers UWEC-2009-01, University of Washington, Department of Economics.
    • Handle: RePEc:udb:wpaper:uwec-2009-01
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    File URL: http://faculty.washington.edu/ezivot/research/afterHoursGarch.pdf
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    References listed on IDEAS

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    Cited by:

    1. Kusen, Alex & Rudolf, Markus, 2019. "Feedback trading: Strategies during day and night with global interconnectedness," Research in International Business and Finance, Elsevier, vol. 48(C), pages 438-463.

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