【2h】

Recursive utility in a Markov environment with stochastic growth

机译:随机增长的马尔可夫环境中的递归效用

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摘要

Recursive utility models that feature investor concerns about the intertemporal composition of risk are used extensively in applied research in macroeconomics and asset pricing. These models represent preferences as the solution to a nonlinear forward-looking difference equation with a terminal condition. In this paper we study infinite-horizon specifications of this difference equation in the context of a Markov environment. We establish a connection between the solution to this equation and to an arguably simpler Perron–Frobenius eigenvalue equation of the type that occurs in the study of large deviations for Markov processes. By exploiting this connection, we establish existence and uniqueness results. Moreover, we explore a substantive link between large deviation bounds for tail events for stochastic consumption growth and preferences induced by recursive utility.
机译:在宏观经济学和资产定价的应用研究中,广泛使用了具有投资者对跨时风险构成关注点的递归效用模型。这些模型将偏好表示为具有终端条件的非线性前瞻性差分方程的解。在本文中,我们研究了在马尔可夫环境下该差分方程的无限水平规范。我们建立了该方程的解与一个可能更简单的Perron–Frobenius特征值方程之间的联系,该方程在研究马尔可夫过程的大偏差时出现。通过利用这种联系,我们建立了存在性和唯一性结果。此外,我们探索了用于随机消费增长的尾部事件的大偏差边界与由递归效用引起的偏好之间的实质联系。

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