A New Variance Bound on the Stochastic Discount Factor
In this paper, we construct a new variance bound on any stochastic discount factor (SDF) of the form m = m(x), with x being a vector of state variables, which tightens the well-known Hansen-Jagannathan bound by a ratio of one over the multiple correlation coefficient between x and the standard minimum variance SDF, m0. In many applications, the correlation is small, and hence the bound is much improved. For example, when x is the growth rate of consumption, the new variance bound can be 25 times greater than the Hansen-Jagannathan bound, making it much more difficult to explain the equity-premium puzzle.
Year of publication: |
2006
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Authors: | Kan, Raymond ; Zhou, Guofu |
Published in: |
The Journal of Business. - University of Chicago Press. - Vol. 79.2006, 2, p. 941-962
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Publisher: |
University of Chicago Press |
Saved in:
Online Resource
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