Footnote:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments October 22, 2021 erstellt
Description:
I propose a novel framework to quantify frequency-dependent risks in the factor zoo. My approach generalizes canonical principal component analysis (PCA) by exploiting frequency-dependent information in asset returns. Empirically, the linear stochastic discount factor (SDF) composed of the first few low-frequency principal components (PCs) capture all the risk premium in asset returns. It also explains well the cross-section of characteristic-sorted portfolios. In contrast, high-frequency and canonical PCA have inferior performance since they fail to identify slow-moving information in asset returns. Moreover, I decompose the low-frequency SDF into two orthogonal priced components. The first component is constructed by high-frequency or traditional PCA. It is almost serially uncorrelated and relates to discount-rate news, intermediary factors, jump risk, and investor sentiment. The second component is slow-moving and captures business-cycle risks related to consumption and GDP growth. Hence, only low-frequency PCA identifies the second persistent component emphasized by many macro-finance models