Stock return predictive relations found to be elusive when using raw data may hold true for different low-frequency components of the data. Similarly, cross-sectional asset pricing models shown not to be supported by raw data may be satisfied when risk is suitably defined with respect to low-frequency components of the aggregate series.  Consistent with this premise, the presentation discusses a novel approach to the analysis of financial time series viewed as the result of a cascade of shocks operating at different frequencies. The approach leads to new asset pricing models as well as to formal justifications for existing results in the finance literature relying on aggregation.