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Commodity trade and international risk sharing
Open accessAbstract
This paper evaluates the social gains from international risk sharing in some simple general-equilibrium models with output uncertainty. A simulation model calibrated to selected moments of U.S. and Japanese data estimates the incremental loss from a ban on international portfolio diversification to be on the order of 0.20 percent of output per year. Even the theoretical gains from asset trade may disappear under alternative sets of assumptions on preferences and technology. The paper argues that the small magnitude of potential trade gains may help explain the apparently inconsistent findings of empirical studies on the degree of international capital mobility.
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Citations
Cited by
Price Stability with Imperfect Financial Integration
BENIGNO, P. (2009) · Journal of Money, Credit and Banking · doi:10.1111/j.1538-4616.2008.00201.x · Cited by 361
Asset Prices and Exchange Rates
Pavlova, A., & Rigobon, R. (2007) · Review of Financial Studies · doi:10.1093/revfin/hhm008 · Cited by 328
Sanctions and the Exchange Rate
Itskhoki, O., & Mukhin, D. (2022) · doi:10.2139/ssrn.4098331 · Cited by 12
Metadata
- Authors
- Cole, H. L., & Obstfeld, M.
- Venue
- Journal of Monetary Economics
- Publisher
- Elsevier BV
- Published
- 1991-08
- Volume
- 28
- Issue
- 1
- Pages
- 3-24
- ISSN
- 0304-3932 (ISSN-L)
- OpenAlex
- W3097259473
- Cited by
- 473
- References
- 0
- FWCI
- 12.24
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- CoChat. (2026). Citation check: Commodity trade and international risk sharing. https://app.cochat.ai/papers/10.1016/0304-3932(91)90023-h
- The paper
- Cole, H. L., & Obstfeld, M. (1991). Commodity trade and international risk sharing. Journal of Monetary Economics. https://doi.org/10.1016/0304-3932(91)90023-h
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.