Eigenvalue tests for the number of latent factors in short panels
Informazioni aggiuntive
Autori
Fortin A. P.,
Gagliardini P.,
Scaillet O.
Tipo
Articolo pubblicato in rivista scientifica
Anno
2023
Lingua
Inglese
Sommario
This article studies new tests for the number of latent factors in a large cross-sectional factor model with small time dimension. These tests are based on the eigenvalues of variance–covariance matrices of (possibly weighted) asset returns and rely on either an assumption of spherical errors, or instrumental variables for factor betas. We establish the asymptotic distributional results using expansion theorems based on perturbation theory for symmetric matrices. Our framework accommodates semi-strong factors in the systematic components. We propose a novel statistical test for weak factors against strong or semi-strong factors. We provide an empirical application to U.S. equity data. Evidence for a different number of latent factors according to market downturns and market upturns is statistically ambiguous in the considered subperiods. In particular, our results contradict the common wisdom of a single-factor model in bear markets.
Parole chiave
Factor model, Principal component analysis, Panel data, Large n and fixed T asymptotics, Equity returns
Pagine (o numero dell’articolo)
nbad024
Volume
00
Periodico
Journal of financial econometrics
Diffusione
Licenza
CC BY
Visibilità
Pubblico
Status open access
Hybrid
