Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
In this paper, we present a novel perspective on data filtering and present an innovative wavelet-based approach that leads to improved Value-at-Risk (VaR) forecasts. A separation of financial conditional volatility into short-, mid- and long-run components allows us to study the relevance of these frequency components with respect to a regulatory quality assessment for daily VaR forecasts.