Predicting Stock Volatility Using After-Hours Information
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.
|Date of creation:||Jan 2009|
|Contact details of provider:|| Postal: Box 353330, Seattle, WA 98193-3330|
Web page: http://www.econ.washington.edu/
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- John Y. Campbell, 2001.
"Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk,"
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- Jonathan H. Wright & Tim Bollerslev, 1999. "High frequency data, frequency domain inference and volatility forecasting," International Finance Discussion Papers 649, Board of Governors of the Federal Reserve System (U.S.).
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- Charles Cao & Eric Ghysels & Frank Hatheway, 2000. "Price Discovery without Trading: Evidence from the Nasdaq Preopening," Journal of Finance, American Finance Association, vol. 55(3), pages 1339-1365, 06.
- Beran, Jan & Feng, Yuanhua, 2002. "SEMIFAR models--a semiparametric approach to modelling trends, long-range dependence and nonstationarity," Computational Statistics & Data Analysis, Elsevier, vol. 40(2), pages 393-419, August.
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