A New Variance Bound on the Stochastic Discount Factor

Raymond Kan and Guofu Zhou
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Raymond Kan: University of Toronto

The Journal of Business, 2006, vol. 79, issue 2, 941-962

Abstract: 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.

Date: 2006
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