A Rehabilitation of Stochastic Discount Factor Methodology
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.
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- Raymond Kan & Guofu Zhou, 1999.
"A Critique of the Stochastic Discount Factor Methodology,"
Journal of Finance,
American Finance Association, vol. 54(4), pages 1221-1248, 08.
- Raymond Kan & Guofu Zhou, 1999. "A Critique of the Stochastic Discount Factor Methodology," CEMA Working Papers 12, China Economics and Management Academy, Central University of Finance and Economics.
- Ravi Jagannathan & Zhenyu Wang, 2002. "Empirical Evaluation of Asset-Pricing Models: A Comparison of the SDF and Beta Methods," Journal of Finance, American Finance Association, vol. 57(5), pages 2337-2367, October.
- Ravi Jagannathan & Zhenyu Wang, 2001. "Empirical Evaluation of Asset Pricing Models: A Comparison of the SDF and Beta Methods," NBER Working Papers 8098, National Bureau of Economic Research, Inc.
- Cochrane, John H, 1996. "A Cross-Sectional Test of an Investment-Based Asset Pricing Model," Journal of Political Economy, University of Chicago Press, vol. 104(3), pages 572-621, June. Full references (including those not matched with items on IDEAS)