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A New Variance Bound on the Stochastic Discount Factor
| Content Provider | Semantic Scholar |
|---|---|
| Author | Kan, Raymond Zhou, Guofu |
| Copyright Year | 2004 |
| 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://apps.olin.wustl.edu/faculty/zhou/bsdf.pdf |
| Alternate Webpage(s) | http://www.olin.wustl.edu/faculty/zhou/bsdf.pdf |
| Alternate Webpage(s) | http://www.mgmt.utoronto.ca/~kan/papers/bound.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Cochrane Library Coefficient Conditional entropy Estimated F Factor Fama IM Inspiration function Memory bound function Population Parameter Risk aversion Sample Variance Volatility Whole Earth 'Lectronic Link |
| Content Type | Text |
| Resource Type | Article |
