News-Good or Bad-and Its Impact on Volatility Predictions over Multiple Horizons

• 成果类型: Article
• 署名作者: Chen, Xilong; Ghysels, Eric
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhq071
• 发表日期: 2011
• 页码: 46
• 关键词: MICROSTRUCTURE NOISE; realized variance; return; MODEL; RISK

摘要

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.