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SUMMARY:Semiparametric Conditional Factor Models: Estimation and Inference
DTSTART:20220211T103000
DTEND:20220211T120000
DTSTAMP:20260406T194619Z
UID:41c04832ab7812b430493359c337c989a31e5847fb085c43050fad59
CATEGORIES:Conferences - Seminars
DESCRIPTION:Nikolai Roussanov\, Wharton Uni. of Pennsylvania\nThis paper i
 ntroduces a simple and tractable sieve estimation of semiparametric condit
 ional factor models with latent factors. We establish large-N-asymptotic p
 roperties of the estimators and the tests without requiring large T. We al
 so develop a simple bootstrap procedure for conducting inference about the
  conditional pricing errors as well as the shapes of the factor loadings f
 unctions. These results enable us to estimate conditional factor structure
  of a large set of individual assets by utilizing arbitrary nonlinear func
 tions of a number of characteristics without the need to pre-specify the f
 actors\, while allowing us to disentangle the characteristics' role in cap
 turing factor betas from alphas (i.e.\, undiversifiable risk from misprici
 ng). We apply these methods to the cross-section of individual U.S. stock 
 returns and find strong evidence of large nonzero pricing errors that comb
 ine to produce arbitrage portfolios with Sharpe ratios above 3.
LOCATION:Zoom
STATUS:CONFIRMED
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