Multi-Objective Stochastic Optimal Asset Allocation for DC Pension under Unpredictable Non-Market Disturbances

Weixiang Xu, Jing-gui Gao, Weihai Zhang
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Abstract

In this paper, we mainly solve the optimal asset allocation problem of DC(defined-contribution) pension under unpredictable non-market disturbances. There are only two types of assets which are allowed to be invested: a risk-free cash bond and a risky stock. Particularly, the pension managers in this paper aim to achieve the minimization of the accumulated deviations between the manager's pre-set target and the actual fund scale with less management cost and make the DC pension plan robust under unpredictable non-market disturbances. To achieve the goals, the definition of robustness performance index with respect to DC pension plan is proposed, and a stochastic multi-objective optimal asset allocation model is established based on the definition. By applying the Itô formula and the matching method, the multi-objective optimal portfolio problem is approximate into a sequence optimization problem with LMI-constrained. To solve the sequence optimization problem, a sequence optimization algorithm is developed in this paper. Finally, we give a numerical simulation to demonstrate the effectiveness of our proposed model.
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不可预测非市场扰动下的固定缴费养老金多目标随机最优资产配置
本文主要研究了在不可预测的非市场干扰下固定缴款养老金的最优资产配置问题。只有两种资产允许投资:无风险的现金债券和有风险的股票。具体而言,本文中养老金管理者的目标是在管理成本较小的情况下,实现管理者预先设定的目标与实际基金规模之间的累积偏差最小化,并使DC养老金计划在不可预测的非市场干扰下具有鲁棒性。为实现这一目标,提出了DC养老金计划稳健性绩效指标的定义,并在此基础上建立了随机多目标最优资产配置模型。利用Itô公式和匹配方法,将多目标最优投资组合问题近似为具有lmi约束的序列优化问题。为了解决序列优化问题,本文提出了一种序列优化算法。最后,通过数值仿真验证了该模型的有效性。
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