We examine whether the sign and magnitude of discretely sampled high frequency returns have impact on future volatility predictions. We first let the 'data speak', namely with minimal interference we capture the mapping between returns over short horizons and future volatility over longer...

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...

It is difficult to define news, and many definitions are model-based since part of what is announced is anticipated. Therefore, news is typically 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...

Suppose one uses a parametric density function based on the first four (conditional) moments to model risk. There are quite a few densities to choose from and depending on which is selected, one implicitly assumes very different tail behavior and very different feasible skewness/kurtosis...

The Fourier estimator of Malliavin and Mancino depends on both sample size and a so-called cutting frequency. The latter controls the number of Fourier coefficients to be included, and it also determines how the Fourier estimator responds to market microstructure noise. By examining the finite...