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受限生命周期投资组合优化的深度定价核方法

文章 arXiv papers · 作者: Wenyuan Li et al.
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总结

本文研究个人一生中的投资组合选择问题，其中收入具有随机性，投资包括股票、债券和人寿保险。目标函数考虑消费、死亡给付和终端财富。凸交易约束用于表示无法交易某些资产、禁止卖空或限制借款等情况。

作者通过调整补偿后的资产漂移，构建反映交易约束的人工市场。随后使用对偶变换和深度定价核方法，计算原优化问题的上下界。该方法适用于因定价核涉及条件期望而无法明确写出价值函数的情况。文中指出，约束会降低消费、人寿保险和年金需求以及财富。摘要没有提供数值结果，也未说明所获界限的紧致程度，或不同约束类型下表现如何变化。

核心观点

  • 生命周期模型结合了随机收入、金融资产、人寿保险、消费、死亡给付和终端财富。
  • 凸交易约束可以表示交易、卖空或借款方面的限制。
  • 人工市场通过调整补偿后的资产漂移来纳入约束。
  • 当无法明确写出价值函数时，深度对偶定价核方法可估算上下界。
  • 论文报告称，交易约束会降低消费、保险和年金需求以及财富。

标签

投资组合构建机器学习风险管理固定收益
全文
# Constrained portfolio optimization in a life-cycle model: A deep pricing kernel approach


# Constrained portfolio optimization in a life-cycle model: A deep pricing kernel approach









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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。