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Standard Risk Aversion
Open accessAbstract
This paper introduces the concept of standard risk aversion. A von Neumann-Morgenstern utility function has standard risk aversion if any risk makes a small reduction in wealth more painful (in the sense of an increased reduction in expected utility) also makes any undesirable, independent risk more painful. It is shown that, given monotonicity and concavity, the combination of decreasing absolute risk aversion and decreasing absolute prudence is necessary and sufficient for standard risk aversion. Standard risk aversion is shown to imply not only Pratt and Zeckhauser's 'proper risk aversion" (individually undesirable, independent risks always being jointly undesirable) , but also that being forced to face an undesirable risk reduces the optimal investment in a risky security with and independent return. Similar results are established for the effect of broad class of increases in one risk on the desirability of (or optimal investment in) a second, independent risk.
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Citations
Cited by
Risk Aversion, Wealth, and Background Risk
Guiso, L., & Paiella, M. (2008) · Journal of the European Economic Association · doi:10.1162/jeea.2008.6.6.1109 · Cited by 1,057
Gomes, F., Haliassos, M., & Ramadorai, T. (2021) · Journal of Economic Literature · doi:10.1257/jel.20201461 · Cited by 396
Cui, Y. (. (2022) · International Journal of Bank Marketing · doi:10.1108/ijbm-09-2021-0451 · Cited by 60
Metadata
- Authors
- Kimball, M. S.
- Venue
- Econometrica
- Publisher
- JSTOR
- Published
- 1993-05
- Volume
- 61
- Issue
- 3
- Pages
- 589
- ISSN
- 0012-9682 (ISSN-L)
- OpenAlex
- W2129585938
- Cited by
- 846
- References
- 0
- FWCI
- 5.23
- Type
- journal article
- Language
- en
- Open access
- green
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- Kimball, M. S. (1993). Standard Risk Aversion. Econometrica. https://doi.org/10.2307/2951719
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.