Event Details

Accounting and Finance, Finance seminar to be presented by Olivier Scaillet, University of Geneva. Paper title: Latent Factor Analysis in Short Panels

Olivier Scaillet - Faculté d'économie et de management - UNIGE

Abstract

We develop inferential tools for latent factor analysis in short panels. The pseudo maximum likelihood

setting under a large cross-sectional dimension n and a fixed time series dimension T relies on a diagonal T.

T covariance matrix of the errors without imposing sphericity nor Gaussianity. We outline the asymptotic

distributions of the latent factor and error covariance estimates as well as of an asymptotically uniformly

most powerful invariant (AUMPI) test for the number of factors based on the likelihood ratio statistic. We

derive the AUMPI characterization from inequalities ensuring the monotone likelihood ratio property for

positive definite quadratic forms in normal variables. An empirical application to a large panel of monthly

U.S. stock returns separates month after month systematic and idiosyncratic risks in short subperiods of bear

vs. bull market based on the selected number of factors. We observe an uptrend in the paths of total and

idiosyncratic volatilities while the systematic risk explains a large part of the cross-sectional total variance

in bear markets but is not driven by a single factor. Rank tests show that observed factors, even beyond

the traditional ones, struggle spanning latent factors with a discrepancy between the dimensions of the two

factor spaces decreasing over time.

Keywords: Latent factor analysis, uniformly most powerful invariant test, panel data, large n and fixed T

asymptotics, equity returns. JEL codes: C12, C23, C38, C58, G12.