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SUMMARY:Shrinking the Cross Section
DTSTART:20170908T103000
DTEND:20170908T120000
DTSTAMP:20260501T141652Z
UID:e8a8973cc5c4f0c54d97b17ce878d5495821ebdf9703bb2d38b3d0fa
CATEGORIES:Conferences - Seminars
DESCRIPTION:Stefan NAGEL (Chicago Booth)\nWe construct a robust stochastic
  discount factor (SDF) that summarizes the joint explanatory power of a la
 rge number of cross-sectional stock return predictors. Our method achieves
  robust out-of-sample performance in this high-dimensional setting by impo
 sing an economically motivated prior on SDF coefficients that shrinks the 
 contributions of low-variance principal components of the candidate factor
 s. While empirical asset pricing research has focused on SDFs with a small
  number of characteristics-based factors—e.g.\, the four- or five-factor
  models discussed in the recent literature—we find that such a character
 istics-sparse SDF cannot adequately summarize the cross-section of expecte
 d stock returns. However\, a relatively small number of principal componen
 ts of the universe of potential characteristics-based factors can approxim
 ate the SDF quite well.
LOCATION:UNIL\, Extranef\, room 126 https://planete.unil.ch/plan/?local=EX
 T-126
STATUS:CONFIRMED
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