Predicting Stock Volatility Using After-Hours Information
نویسندگان
چکیده
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. Corresponding Author: Economic and Valuation Service, KPMG LLP, 500 East Middlefield Rd., Mountain View, CA 94043.Email: [email protected]. Tel: +1-650-404-4704. Fax: +1-650-523-4340. We thank Mike Wenz for helpful comments. 2 Economics and Finance Department, Winona State University, Somsen 319E, Winona, MN 55987, USA. Email: [email protected]. Tel: +1-507-457-2982. Fax: +1-507-457-5697. Department of Economics, University of Washington, Box 353330, Condon 401, Seattle, WA 98195. Email: [email protected]. Tel: +1-206-543-6715. Fax: +1-206-685-7477.
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