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2021
- handle: 10670/1.1sjoyc
- hal: hal-04164327
- doi: 10.1016/j.jet.2021.105189
- WOS: 000621273100017
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info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jet.2021.105189
info:eu-repo/semantics/OpenAccess
Mots-clés
Stochastic dominance Risk orders Prudence Temperance Concordance D - Microeconomics/D.D8 - Information, Knowledge, and Uncertainty/D.D8.D81 - Criteria for Decision-Making under Risk and UncertaintySujets proches
Instruction and study--Methods Utility functions Intoxication Intemperance Abstinence Drunkenness Total abstinence Probability Statistical inference Price policy, Industrial Retail pricing Price policy Funds Funding Pattern Model Instruction and study--Methods Finance, commerce, confiscations, etcCiter ce document
Christian Gollier, « A general theory of risk apportionment », HAL-SHS : économie et finance, ID : 10.1016/j.jet.2021.105189
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Résumé
Suppose that the conditional distributions of x˜ (resp. y˜) can be ranked according to the m-th (resp. n-th) risk order. Increasing their statistical concordance increases the (m, n) degree riskiness of (˜x, y˜), i.e., it reduces expected utility for all bivariate utility functions whose sign of the (m, n) cross-derivative is (−1)m+n+1. This means in particular that this increase in concordance of risks induces a m + n degree risk increase in x˜ + ˜y. On the basis of these general results, I provide different recursive methods to generate high degrees of univariate and bivariate risk increases. In the reverse-or-translate (resp. reverse-or-spread) univariate procedure, a m degree risk increase is either reversed or translated downward (resp. spread) with equal probabilities to generate a m + 1 (resp. m + 2) degree risk increase. These results are useful for example in asset pricing theory when the trend and the volatility of consumption growth are stochastic or statistically linked.