Stefano Giglio Publications

We show that two important issues in empirical asset pricing—the presence of weak factors and the selection of test assets—are deeply connected. Since weak factors are those to which test assets have limited exposure, an appropriate selection of test assets can improve the strength of factors. Building on this insight, we introduce supervised principal component analysis (SPCA), a methodology that iterates supervised selection, principal-component estimation, and factor projection. It enables risk premia estimation and factor model diagnosis even when weak factors are present and not all factors are observed. We establish SPCA's asymptotic properties and showcase its empirical applications.

We use a large cross section of equity returns to estimate a rich affine model of equity prices, dividends, returns, and their dynamics. Our model prices dividend strips of the market and equity portfolios without using strips data in the estimation. Yet model-implied equity yields closely match yields on traded strips. Our model extends equity term-structure data over time (to the 1970s) and across maturities, and generates term structures for various equity portfolios. The novel cross section of term structures from our model covers 45 years and includes several recessions, providing a novel set of empirical moments to discipline asset pricing models.