[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.]

We examine whether the sign and magnitude of intra-daily returns have impact on expected volatility the next day or over longer future horizons. We first let the ’data speak’, namely with minimal interference we capture the mapping between intra-daily returns and future volatility. We 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. We also introduce a new class of parametric models which feature asymmetries and with close ties to ARCH-type models, albeit applicable to a mixture of high and low frequency data. Models featuring asymmetries dominate, especially during the 2007-2008 financial crisis. ∗We like to thank Oliver Linton for comments and sharing with us software. In addition, we like to thank the Referees and the Editor for many helpful suggestions and comments on a previous version of