Time-Varying Risk Premium in Large Cross-Sectional Equity Data Sets

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Autoencoder asset pricing models (2020, methodology): “Autoencoders are critical tools in the machine learning suite that have enjoyed 1Other related asset pricing papers include Feng et al. (2019b), Freyberger et al. (2017), Kozak et al. (2017), Feng et al. (2019a), Giglio and Xiu (2018), and Gagliardini et al. (2016), among others.”
The Incidental Parameters Problem in Testing for Remaining Cross-Section Correlation (2018, background): “However, in addition to having severe conceptual shortcomings (as discussed in Gagliardini, Ossola, and Scaillet 2016) such an approach would lead to incorrect conclusions concerning the power of a CD test statistic for remaining cross-section correlation.”
Taming the Factor Zoo: A Test of New Factors (2019, unknown): “Recent literature, including Bai and Zhou (2015), Gagliardini, Ossola, and Scaillet (2016), Gagliardini, Ossola, and Scaillet (2019), Connor, Hagmann, and Linton (2012), Giglio and Xiu (2016), and Raponi, Robotti, and Zaffaroni (2017), relies on alternative asymptotic designs for better…”
A diagnostic criterion for approximate factor structure (2016, background): “…is not correctly specified, the no-arbitrage restrictions derived from APT will not hold, and the risk premia estimated by the two-pass regression approach will be meaningless (see Appendix H in (Gagliardini et al., 2016, GOS) for a discussion of misspecification in the two-pass methodology).”