Risk Aversion in the Small and in the Large under Rank-Dependent Utility

By Louis Eeckhoudt, Roger J. A. Laeven. 2015

606 citations recorded by the source corpus.

Under expected utility the local index of absolute risk aversion has played a central role in many applications. Besides, its link with the "global" concepts of the risk and probability premia has reinforced its attractiveness. This paper shows that, with an appropriate approach, similar developments can be achieved in the framework of Yaari's dual theory and, more generally, under rank-dependent utility.

Subfields: General Decision Sciences, Management Science and Operations Research, Statistics and Probability

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Citations in the corpus

Variational Bayesian Reinforcement Learning with Regret Bounds (2018, background): “(1) For τ ≤ 0 this utility function is risk-averse and has been studied extensively in the literature on safety and risk-averse reinforcement learning (Howard and Matheson, 1972; Pratt, 1975; Marcus et al., 1997; Mihatsch and Neuneier, 2002; Liu et al., 2003; Maddison et al., 2017).”
Supply‐Chain Research Opportunities with the Poor as Suppliers or Distributors in Developing Countries (2014, background): “To model risk aversion associated with any amount of money z , assume the farmer has a utility function U ( z ) = 1 – e -rz , where r > 0 represents the coefﬁcient of constant absolute risk aversion (e.g., Pratt 1964).”
Risk Aversion and Guanxi Activities: A Behavioral Analysis of CEOs in China (2017, background): “To elicit risk preferences, we rely on a standard price list form of risk elicitation widely used in experimental and behavioral studies (Binswanger, 1980; Holt & Laury, 2002).”
Smart Shopping Carts: How Real-Time Feedback Influences Spending (2013, neutral): “This margin can be viewed as a monetary measure of the implicit “cost” of bearing the risk to exceed their budget (Arrow 1971; Pennings and Wansink 2004; Pratt 1964).”
The Altruistic Rich? Inequality and Other-Regarding Preferences for Redistribution (2017, background): “One implication of this specification is that u exhibits the Arrow-Pratt measure of constant relative risk aversion (CRRA; Pratt 1964): R(c) = −cu′′(c)/u′(c).”
A Theory of Rational Demand for Index Insurance (2016, background): “…as it is more commonly known in the context of 2 This restriction on the utility function is equivalent to assuming that, endowed with certain wealth, such an individual would never accept a non-degenerate lottery if the expected net gain from the lottery was nonpositive (Pratt 1964, Arrow 1965).”
Particle Value Functions (2017, background): “This choice is well-studied, and it is implied by the assumption that V πT ( s, u ) is additive for deterministic translations of the reward function (Pratt, 1964; Howard & Matheson, 1972; Coraluppi, 1997).”
Impact of Violent Crime on Risk Aversion: Evidence from the Mexican Drug War (2019, neutral): “These models make several strong assumptions such as the form of the utility function, whether gamble amounts are integrated with personal wealth, whether savings are allowed, and whether background risk is accounted for (Arrow, 1970; Pratt, 1964; Gollier, 2000).”
Measuring risk attitudes among Mozambican farmers (2014, background): “In this case, the most commonly adopted measures of risk aversion are given by the coefficient of absolute risk aversion ARA(x) = – , or by the coefficient of relative risk aversion RRA(x) = x ARA(x) (Pratt 1964; Arrow 1965).”
What Do Data on Millions of U.S. Workers Reveal about Life-Cycle Earnings Risk? (2015, background): “Consider the well-known thought experiment (Arrow (1965); Pratt (1964)), in which a decision maker chooses between (i) a static gamble that changes consumption, c, by a random proportion (1 + ˜ ) and (ii) a fixed payment ⇡, called the risk premium, to avoid the gamble.”
Action and Perception as Divergence Minimization (2020, methodology): “…overconﬁdence (Jaynes, 1957), Occam’s razor to prevent overﬁtting (Jefferys and Berger, 1992), bounded rationality to halt optimization before reaching the point solution (Ortega and Braun, 2011), and risk-sensitivity to account for model misspeciﬁcation (Pratt, 1964; Howard and Matheson, 1972).”