This thesis focuses on the propagation of scale-specific (i.e., horizon-dependent) macroeconomic shocks into asset prices. In particular, chapter 1 provides an introduction to the theory and methods necessary for understanding scale-dependencies in financial economics. First, I present the multiresolution-based decompositions for weakly stationary time series of Ortu et al. (2013) and discuss its connection with other techniques in the literature. Next, I analyse the power and size properties of multi-scale variance ratio tests that distinguish a white noise process from a process whose scale-dependent components are serially correlated. Finally, I present an extension of the framework of Bandi et al. (2016) for scale-specific predictability. In chapter 2, I show that a single factor that captures assets' exposure to business-cycle variation in macroeconomic uncertainty can explain the level and cross-sectional differences of asset returns. In addition, I find that - in contrast with previous studies in the literature - macro uncertainty is not a valid risk factor under the ICAPM. Chapter 3 provides an empirical assessment of Epstein-Zin preferences in the frequency domain. I demonstrate that the strict conditions implied by the spectral decomposition of recursive preferences are not empirically satisfied. That is, macroeconomic shocks with frequencies lower than the business-cycle are not robustly priced in asset prices.