|
John H. Cochrane
NBER Working Paper No. 8533
Issued in October 2001
NBER Program(s): AP
---- Abstract -----
In a recent Journal of Finance article, Kan and Zhou (1999) find that the 'Stochastic discount factor' methodology using GMM is markedly inferior to traditional maximum likelihood even in a simple test of the static CAPM with i.i.d. normal returns. This result has gained wide attention. However, as Jagannathan and Wang (2001) point out, this result flows from a strange assumption: Kan and Zhou allow the ML estimate to know the mean market return ex-ante. I show how this information advantage explains Kan and Zhou's results. In fact, when treated symmetrically, the discount factor - GMM and traditional methodologies behave almost identically in linear i.i.d. environments.
Would you like an annual subscription to NBER Working Papers? Click
here for more information.
You may purchase this paper on-line in .pdf format
from SSRN.com ($5) for electronic delivery.
Information for subscribers and others expecting no-cost downloads
Machine-readable bibliographic record -
MARC,
RIS,
BibTeX
| 
