TY  - JOUR
AU  - Cochrane,John H.
TI  - A Rehabilitation of Stochastic Discount Factor Methodology
JF  - National Bureau of Economic Research Working Paper Series
VL  - No. 8533
PY  - 2001
Y2  - October 2001
UR  - http://www.nber.org/papers/w8533
L1  - http://www.nber.org/papers/w8533.pdf
N1  - Author contact info:
John H. Cochrane
Booth School of Business
University of Chicago
5807 S. Woodlawn
Chicago, IL  60637
Tel: 773/702-3059
Fax: 773/702-0458
E-Mail: john.cochrane@chicagobooth.edu
AB  - 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.
ER  -