生命周期模型中的约束投资组合优化：一种深度定价核方法
Constrained portfolio optimization in a life-cycle model: A deep pricing kernel approach
AI总结：
本文在广义生命周期模型中，通过深度定价核方法求解凸集交易约束下的投资组合优化问题，并证明了对偶问题与原问题的等价性，最后分析了约束对消费、保险和财富的影响。
摘要：
本文考虑了广义生命周期模型中的约束投资组合优化。具有随机收入的个体管理由股票、债券和人寿保险组成的投资组合，以最大化其消费水平、死亡抚恤金和最终财富。同时，个体面临凸集交易约束，其中不可交易资产约束、无卖空约束和无借款约束是特殊情况。我们通过操纵基础资产的补偿漂移项以满足交易约束，构建人工市场来解决该问题。通过对偶变换，建立对偶问题与原问题之间的一一对应关系，并利用松弛投影技术证明了原问题的存在性。此外，我们提出了一种对偶控制深度定价核方法，用于计算原问题的紧致上下界，当由于定价核的条件期望导致值函数缺乏显式解时，该方法可被使用。最后，我们得出结论：当考虑交易约束时，由于市场受限，个体将减少其消费、人寿保险和年金需求以及财富水平。
英文摘要：
This paper considers the constrained portfolio optimization in a generalized life-cycle model. The individual with a stochastic income manages a portfolio consisting of stocks, a bond, and life insurance to maximize their consumption level, death benefit, and terminal wealth. Meanwhile, the individual faces a convex-set trading constraint, with the non-tradeable asset constraint, no short-selling constraint, and no borrowing constraint as special cases. We build the artificial markets to solve this problem by manipulating the compensated drift terms of the underlying assets to meet the trading constraints. By dual transform, we propose a deep pricing kernel approach to compute tight lower and upper bounds for the primal problem, which can be used when the value function lacks an explicit solution due to the pricing kernel's conditional expectation. Finally, we conclude that when considering the trading constraints, the individual will reduce their consumption, demand for life insurance and annuities, and wealth levels due to the restricted market.
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。
