递归偏好、相关厌恶与不确定性的时间分辨率
Recursive Preferences, Correlation Aversion, and the Temporal Resolution of Uncertainty
AI总结：
本文研究递归偏好中的新行为特征：对持久风险的厌恶，即相关厌恶。持久性提供未来消费信息但减少消费风险对冲机会。作者证明，对于偏好早期解决不确定性的递归偏好，相关厌恶等同于递增相对风险厌恶。通过发展持久性溢价概念量化相关厌恶，并展示其在资产组合选择和宏观金融模型校准中的应用。
摘要：
本文研究递归偏好中的一种新行为特征：对持久风险的厌恶，即相关厌恶。持久性提供未来消费信息但减少消费风险对冲机会。作者证明，对于偏好早期解决不确定性的递归偏好，相关厌恶等同于递增相对风险厌恶。为量化相关厌恶，作者发展了持久性溢价的概念，该概念衡量个体愿意为消除消费中的持久性支付多少。作者以Arrow-Pratt的方式近似持久性溢价，提供信息与对冲权衡的量化表示。作者展示相关厌恶偏好具有变分表示，将相关厌恶与模型误 specification 的担忧联系起来。本文提供了多个应用：首先展示相关厌恶如何影响资产组合选择，然后展示持久性溢价如何改进宏观金融模型的校准。在最优税收模型中，作者证明递归偏好——不同于标准偏好——导致再分配税收政策，增加社会流动性。
英文摘要：
This paper investigates a novel behavioral feature of recursive preferences: aversion to risks that persist over time, or simply \textit{correlation aversion}. Greater persistence provides information about future consumption but reduces opportunities to hedge consumption risk. I show that, for recursive preferences that exhibit a preference for early resolution of uncertainty, correlation aversion is equivalent to increasing relative risk aversion. To quantify correlation aversion, I develop the concept of the persistence premium, which measures how much an individual is willing to pay to eliminate persistence in consumption. I provide an approximation of the persistence premium in the spirit of Arrow--Pratt, which provides a quantitative representation of the trade-off between information and hedging. I show that correlation-averse preferences have a variational representation, linking correlation aversion to concerns about model misspecification. I present several applications. I first illustrate how correlation aversion shapes portfolio choices, and then show how the persistence premium can improve the calibration of macro-finance models. In an optimal taxation model, I show that recursive preferences -- unlike standard preferences -- lead to redistributive tax policies that increase social mobility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
