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We show that standard beta pricing models quantify an asset's systematic risk as a weighted combination of a number of different timescale betas. Given this, we develop a wavelet-based framework that examines the cross-sectional pricing implications of isolating these timescale betas. An empirical application to the Fama-French model reveals that the model's wellknown empirical success is largely due to the beta components associated with a timescale just short of a business cycle (i.e., wavelet scale 3). This implies that any viable explanation for the success of the Fama-French model that has been applied to the Fama-French factors should apply particularly to the scale 3 components of the factors. We find that a risk-based explanation conforms closely to this implication. This paper presents a framework that characterizes an asset's systematic risk in terms of the asset's exposures to a number of different timescale (e.g., short-, intermediate-, long-run) fluctuations in a model's factors. We call measures of such exposures timescale betas. Using two fundamental results in wavelet theory, we show that an asset's standard betas, or factor loadings, can be written as a linear combination of the asset's timescale betas. Empirically, we isolate these betas and explore their relative importance for the cross section of returns.Our exploration is motivated in spirit by the long-run consumption-risk literature, which suggests the possibility that only part of the information in the standard betas is relevant for asset pricing and the relevant part is concentrated in certain timescale betas (see, e.g.,

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