A New Variance Bound on the Stochastic Discount Factor*

Abstract

In this paper, we construct a new variance bound on any stochastic discount factor (SDF) of the form m = m(x), where x is a vector of random state variables. In contrast to the well known Hansen-Jagannathan bound that places a lower bound on the variance of m(x), our bound tightens it by a ratio of 1/ρ 2 x,m0 where ρx,m0 is the multiple correlation coefficient between x and the standard minimum variance SDF, m0. In many applications, the correlation is small, and hence our bound can be substantially tighter than Hansen-Jagannathan’s. For example, when x is the growth rate of consumption, based on Cochrane’s (2001) estimates of market volatility and ρx,m0, the new variance bound is 25 times greater than the Hansen-Jagannathan bound, making it much more difficult to explain the equity-premium puzzle based on existing asset pricing models.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 22 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.