Working Paper: NBER ID: w8533
Authors: John H. Cochrane
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.
Keywords: No keywords provided
JEL Codes: No JEL codes provided
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| SDF methodology (C88) | inferior performance compared to traditional ML estimates (C51) |
| assumptions about mean market return (G17) | SDF methodology (C88) |
| assumptions about mean market return (G17) | traditional ML estimates (C51) |
| false information advantage (D83) | estimation accuracy of ML over SDF (C52) |
| SDF methodology treated symmetrically (C69) | similar results to traditional ML estimates (C51) |