Abstract
AbstractIn this article we consider the post-retirement phase optimization problem for a specific pension product in Germany that comes without guarantees. The continuous-time optimization problem is defined consisting of two specialties: first, we have a product-specific pension adjustment mechanism based on a certain capital coverage ratio which stipulates compulsory pension adjustments if the pension fund is underfunded or significantly overfunded. Second, due to the retiree’s fear of and aversion against pension reductions, we introduce a total wealth distribution to an investment portfolio and a buffer portfolio to lower the probability of future potential pension shortenings. The target functional in the optimization, that is to be maximized, is the client’s expected accumulated utility from the stochastic future pension cash flows. The optimization outcome is the optimal investment strategy in the proposed model. Due to the inherent complexity of the continuous-time framework, the discrete-time version of the optimization problem is considered and solved via the Bellman principle. In addition, for computational reasons, a policy function iteration algorithm is introduced to find a stationary solution to the problem in a computationally efficient and elegant fashion. A numerical case study on optimization and simulation completes the work with highlighting the benefits of the proposed model.
Funder
Technische Universität München
Publisher
Springer Science and Business Media LLC
Subject
Statistics, Probability and Uncertainty,Economics and Econometrics,Statistics and Probability
Reference25 articles.
1. aba and IVS (2017) Die reine Beitragszusage gemäß dem Betriebsrentenstärkungsgesetz. Tech. rep., aba Arbeitsgemeinschaft für betriebliche Altersversorgung e. V. and IVS—Institut der Versicherungsmathematischen Sachverständigen für Alterversorgung e. V., Berlin
2. Bellman R (1952) On the theory of dynamic programming. Proc Natl Acad Sci 38:716–719
3. Bellman R (1955) Functional equations in the theory of dynamic programming. V. Positivity and quasi-linearity. Proc Natl Acad Sci USA 41(10), 743–746
4. Bellman R (1957) Dynamic programming. Princeton University Press, Princeton
5. Bellman R (1958) Dynamic programming and stochastic control processes. Inf Control 1:228–239
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献