News—Good or Bad—and Its Impact on Volatility Predictions over Multiple Horizons
提出新参数模型，研究日内高频与低频收益混合下的新闻冲击曲线，发现适度好消息降低次日波动，而极好消息和坏消息均增加波动，且不对称性随预测期延长消失，模型在金融危机期间预测表现更优。
We introduce a new class of parametric models applicable to a mixture of high and low frequency returns and revisit the concept of news impact curves introduced by Engle and Ng (1993). Overall, we find that moderately good (intra-daily) news reduces volatility (the next day), while both very good news (unusual high intra-daily positive returns) and bad news (negative returns) increase volatility, with the latter having a more severe impact. The asymmetries disappear over longer horizons. Models featuring asymmetries dominate in terms of out-of-sample forecasting performance, especially during the 2007--2008 financial crisis. The Author 2010. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org., Oxford University Press.