部分信息下的最优退休规划

IF 1.3 Q2 STATISTICS & PROBABILITY Statistics & Risk Modeling Pub Date : 2018-06-29 DOI:10.1515/strm-2018-0027
N. Bäuerle, A. Chen
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引用次数: 6

摘要

摘要本文分析了面对金融市场参数不确定性的具有恒定相对风险厌恶(CRRA)的退休人员的最优消费与投资问题。我们通过使市场观测完全,从而应用鞅方法得到最优消费和投资策略的闭型解,解决了部分信息下的最优问题。此外,我们还提供了一些比较统计和数值分析,以深入了解部分信息下的消费和投资行为。承担部分信息对最优消费水平的影响不大,但使RRA小于1的退休人员投资风险更大,而使RRA大于1的退休人员投资更保守。
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Optimal retirement planning under partial information
Abstract The present paper analyzes an optimal consumption and investment problem of a retiree with a constant relative risk aversion (CRRA) who faces parameter uncertainty about the financial market. We solve the optimization problem under partial information by making the market observationally complete and consequently applying the martingale method to obtain closed-form solutions to the optimal consumption and investment strategies. Further, we provide some comparative statics and numerical analyses to deeply understand the consumption and investment behavior under partial information. Bearing partial information has little impact on the optimal consumption level, but it makes retirees with an RRA smaller than one invest more riskily, while it makes retirees with an RRA larger than one invest more conservatively.
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来源期刊
Statistics & Risk Modeling
Statistics & Risk Modeling STATISTICS & PROBABILITY-
CiteScore
1.80
自引率
6.70%
发文量
6
期刊介绍: Statistics & Risk Modeling (STRM) aims at covering modern methods of statistics and probabilistic modeling, and their applications to risk management in finance, insurance and related areas. The journal also welcomes articles related to nonparametric statistical methods and stochastic processes. Papers on innovative applications of statistical modeling and inference in risk management are also encouraged. Topics Statistical analysis for models in finance and insurance Credit-, market- and operational risk models Models for systemic risk Risk management Nonparametric statistical inference Statistical analysis of stochastic processes Stochastics in finance and insurance Decision making under uncertainty.
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