Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We propose a novel approach to the modelling and forecasting of high-frequency trading volumes. The new model extends the component multiplicative error model of Brownlees et al. (2011) by introducing a more flexible specification of the long-run component. This uses an additive cascade of MIDAS polynomial filters, moving at different frequencies, to reproduce the changing long-run level and the persistent autocorrelation structure of high-frequency trading volumes. After investigating the statistical properties of the proposed approach, we illustrate its merits by means of an application to six stocks that are traded on the XETRA market in the German Stock Exchange.