Year: 2021
Type: report
Abstract: 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 applying it to estimate the risk premia of several tradable and nontradable factors, to evaluate asset managers' performance, and to de-noise asset pricing factors.
DOI: 10.3386/w29002
Authors: Stefano Giglio, Dacheng Xiu, Dake Zhang
Institutions: Rutgers, The State University of New Jersey, University of North Carolina at Chapel Hill, National Bureau of Economic Research, University of Connecticut, University of Miami, University of California, Los Angeles, The University of Texas at Dallas, Duke University, Chinese University of Hong Kong, Tilburg University, Cornell University, University of Michigan, Yale University, University of Chicago, INSEAD, INSEAD, Université Paris Dauphine-PSL, Temple University, London School of Economics and Political Science
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W3124688991