具有最小担保约束和隐马尔可夫制度切换的DC养老金计划的资产配置

IF 0.7 3区 工程技术 Q4 ENGINEERING, INDUSTRIAL Probability in the Engineering and Informational Sciences Pub Date : 2022-11-21 DOI:10.1017/s0269964822000419
Liuling Luo, Xingchun Peng
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引用次数: 1

摘要

本文研究了隐马尔可夫制度切换经济条件下具有最低担保约束的养老金计划的资产配置问题。假设金融市场上有四种类型的资产:无风险资产、零息债券、通胀指数债券和股票。股票的期望收益率依赖于不可观测的经济状态,状态的变化用隐马尔可夫链来描述。此外,还利用CIR过程来描述名义利率的演变。贡献率也被假定为随机的。投资管理的目标是使终端财富超过最低担保约束的凸风险测度最小化。首先,利用Wonham滤波技术将部分可观察优化问题转化为完全信息优化问题,并通过构造辅助过程来处理最小保证约束。在此基础上,利用BSDE方法推导出最优投资策略。最后,给出了一些数值结果来说明一些重要参数对投资行为的影响。
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Asset allocation for a DC pension plan with minimum guarantee constraint and hidden Markov regime-switching
This paper is devoted to the study of the asset allocation problem for a DC pension plan with minimum guarantee constraint in a hidden Markov regime-switching economy. Suppose that four types of assets are available in the financial market: a risk-free asset, a zero-coupon bond, an inflation-indexed bond and a stock. The expected return rate of the stock depends on unobservable economic states, and the change of states is described by a hidden Markov chain. In addition, the CIR process is used to describe the evolution of the nominal interest rate. The contribution rate is also assumed to be stochastic. The goal of investment management is to minimize the convex risk measure of the terminal wealth in excess of the minimum guarantee constraint. First, we transform the partially observable optimization problem into the one with complete information using the Wonham filtering technique and deal with the minimum guarantee constraint by constructing auxiliary processes. Furthermore, we derive the optimal investment strategy by the BSDE approach. Finally, some numerical results are presented to illustrate the impacts of some important parameters on investment behaviors.
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来源期刊
CiteScore
2.20
自引率
18.20%
发文量
45
审稿时长
>12 weeks
期刊介绍: The primary focus of the journal is on stochastic modelling in the physical and engineering sciences, with particular emphasis on queueing theory, reliability theory, inventory theory, simulation, mathematical finance and probabilistic networks and graphs. Papers on analytic properties and related disciplines are also considered, as well as more general papers on applied and computational probability, if appropriate. Readers include academics working in statistics, operations research, computer science, engineering, management science and physical sciences as well as industrial practitioners engaged in telecommunications, computer science, financial engineering, operations research and management science.
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