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Latent Factor Analysis in Short Panels

Alain-Philippe Fortin, Patrick Gagliardini and Olivier Scaillet
Additional contact information
Alain-Philippe Fortin: University of Geneva; Swiss Finance Institute
Patrick Gagliardini: Università della Svizzera italiana; Swiss Finance Institute

No 23-44, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

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 x T covariance matrix of the errors without imposing sphericity or 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 based on the likelihood ratio statistic for tests of the number of factors. 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 date after date systematic and idiosyncratic risks in short subperiods of bear vs. bull market based on the selected number of factors. We observe an uptrend in idiosyncratic volatility 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 reveal that observed factors struggle spanning latent factors with a discrepancy between the dimension of the two factor spaces decreasing over time.

Keywords: Latent factor analysis; uniformly most powerful invariant test; panel data; large n and fixed T (search for similar items in EconPapers)
JEL-codes: C12 C23 C38 C58 G12 (search for similar items in EconPapers)
Pages: 72 pages
Date: 2023-06
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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Working Paper: Latent Factor Analysis in Short Panels (2024) Downloads
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