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A New Variance Bound on the Stochastic Discount Factor

  • Raymond Kan

    (University of Toronto)

  • Guofu Zhou

    (Washington University in St. Louis)

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.

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Article provided by University of Chicago Press in its journal Journal of Business.

Volume (Year): 79 (2006)
Issue (Month): 2 (March)
Pages: 941-962

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Handle: RePEc:ucp:jnlbus:v:79:y:2006:i:2:p:941-962
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