Rare Disasters and Risk Sharing with Heterogeneous Beliefs
By Hui Chi Chen, Scott Joslin, Ngoc-Khanh Tran. 2011
150 citations recorded by the source corpus.
Risks of rare economic disasters can have large impact on asset prices. At the same time, difficulty in inference regarding both the likelihood and severity of disasters as well as agency problems can effectively lead to signiffcant disagreements among investors about disaster risk. We show that such disagreements generate strong risk sharing motives, such that just a small amount of optimists in the economy can significantly reduce the disaster risk premium. Our model highlights the "latent" nature of disaster risk: the disaster risk premium will likely be low and smooth during normal times, but can increase dramatically when the risk sharing capacity of the optimists is reduced, for example, following a disaster. The model also helps reconcile the difference in the amount of disaster risk implied by nancial markets and international macro data, and provides caution to the approach of extracting disaster probabilities from asset prices, which can disproportionately reflect the beliefs of a small group of optimists. Finally, our model predicts an inverse U-shaped relation between the equity premium and the size of the disaster insurance market.
Subfields: Economics and Econometrics, Finance, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/9ab71f6942c867ec2f3cf1da8d4a5673a0ef5419
How researchers see this paper
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In Economics and Econometrics
- Pierre Collin-Dufresne
- Lars A. Lochstoer
- Michael Johannes
- Ole E. Barndorff-Nielsen
- Tim Bollerslev
- F. Bandi
- N. Shephard
- Torben G. Andersen
In Finance
- Pierre Collin-Dufresne
- Lars A. Lochstoer
- Michael Johannes
- Tim Bollerslev
- Torben G. Andersen
- Mikhail Chernov
- N. Shephard
- F. Bandi
In Management Science and Operations Research
Citations in the corpus
- Parameter Learning in General Equilibrium: The Asset Pricing Implications (2016, methodology): “In particular, we assume that log aggregate consumption growth within paradigm s is given by: ∆ct+1 = μs + σεt+1, (19)”