News - good or bad - and its impact over multiple horizons Xilong Chen, Eric Ghysels* ( University of North Carolina - Chapel Hill) Abstract: It is difficult to define news, and many definitions are de facto model-based
since part of what is announced is anticipated. Therefore, news is defined
as a residual within the context of some type of prediction model, and
the prediction model locks in the sampling frequency that is the reference
time scale for analyzing propagation mechanisms. We try toaccomplish two
goals: (1) characterize news as much as possible as a model-free observation,
and (2) measure the impact ofnews over any arbitrary horizon of interest.
We revisit the concept of news impact curves introduced by Engle and Ng
(1993), in the current data rich environment of financial market time
series. Instead of taking a single horizon fixed parametric specification,
we recast many of the original ideas in a very flexible multi-horizon
semi-parametric setting. We find that moderately good (intra-daily) news
reduces volatility (the next day), while both very good news (unusual
high positive returns) and bad news(negative returns) increase volatility,
with the latter having amore severe impact. The asymmetries we find have
profound implications for current volatility prediction models that are
based on in-sample asymptotic analysis developed over recent years. Technically
speaking we introduce semi-parametric MIDAS regressions and study their
asymptotic properties. The analysis relates to and extends recent work
by Linton and Mammen (2005). In addition we also introduce various new
parametric models. |