Test Assets and Weak Factors

Estimation and testing of factor models in asset pricing requires choosing a set of test assets. The

choice of test assets determines how well different factor risk premia can be identified: if only few assets

are exposed to a factor, that factor is weak, which makes standard estimation and inference incorrect.

In other words, the strength of a factor is not an inherent property of the factor: it is a property of the

cross-section used in the analysis. We propose a novel way to select assets from a universe of test assets

and estimate the risk premium of a factor of interest, as well as the entire stochastic discount factor, that

explicitly accounts for weak factors and test assets with highly correlated risk exposures. We refer to our

methodology as supervised principal component analysis (SPCA), because it iterates an asset selection

step and a principal-component estimation step. We provide the asymptotic properties of our estimator,

and compare its limiting behavior with that of alternative estimators proposed in the recent literature,

which rely on PCA, Ridge, Lasso, and Partial Least Squares (PLS). We find that the SPCA is superior

in the presence of weak factors, both in theory and in finite samples. We illustrate the use of SPCA by

using it to estimate the risk premia of several tradable and non-tradable factors.

Zoom link: https://eur-nl.zoom.us/j/94699390864?pwd=UEJSWDBxU3o0MjAvVXBHbTA4ZWpKQT09 

Meeting ID: 946 9939 0864

Passcode: 769980