  This article explores the existing normalizing and variance-stabilizing
(NoVaS) method on predicting squared log-returns of financial data. First, we
explore the robustness of the existing NoVaS method for long-term
time-aggregated predictions. Then we develop a more parsimonious variant of the
existing method. With systematic justification and extensive data analysis, our
new method shows better performance than current NoVaS and standard GARCH(1,1)
methods on both short- and long-term time-aggregated predictions.
