动态联盟投资组合选择与递归效用
Dynamic Coalition Portfolio Selection with Recursive Utility
AI总结：
本文针对动态联盟投资组合选择问题，基于Epstein-Zin递归效用，通过前向-后向SDE和动态博弈方法求解均衡帕累托策略，发现递归效用下消费策略更有效，且风险厌恶与EIS关系影响更大。
摘要：
本文考虑了一个动态联盟投资组合选择问题，其中每个代理的目标由Epstein-Zin递归效用给出。为了找到帕累托最优，联盟问题被表述为一个由多维前向-后向随机微分方程（SDE）演化的优化问题。由于演化系统具有前向-后向结构，该问题本质上具有时间不一致性。从动态博弈的角度，我们严格发展了一种寻找均衡帕累托投资-消费策略的方法。我们发现，风险厌恶与EIS之间的关系对联盟问题的影响大于对单代理问题的影响。更有趣的是，我们表明与递归效用相关的均衡帕累托消费策略比与CRRA期望效用相关的策略有效得多，这突显了递归效用的特点，即消费的边际收益可以依赖于未来消费。
英文摘要：
In this paper, we consider a dynamic coalition portfolio selection problem, with each agent's objective given by an Epstein--Zin recursive utility. To find a Pareto optimum, the coalition's problem is formulated as an optimization problem evolved by a multi-dimensional forward-backward SDE. Since the evolution system has a forward-backward structure, the problem is intrinsically time-inconsistent. With the dynamic-game point of view, we rigorously develop an approach to finding the equilibrium Pareto investment-consumption strategy. We find that the relationship between risk aversion and EIS has more influence on the coalition's problem than that on the one-agent problem. More interestingly, we show that the equilibrium Pareto consumption strategy associated with the recursive utility is much more effective than that associated with the CRRA expected utility, which highlights the feature of recursive utilities that the marginal benefit of consumption can depend on the future consumption.
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。
