paper-with-me

홈 › Papers

Achieving a Given Financial Goal with Optimal Deferred Term Insurance Purchasing Policy

2022-12-09 · Yuqi Li, Lihua Zhang

This paper researches the problem of purchasing deferred term insurance in the context of financial planning to maximize the probability of achieving a personal financial goal. Specifically, our study starts from the perspective of hedging death risk and longevity risk, and considers the purchase of deferred term life insurance and deferred term pure endowment to achieve a given financial goal for the first time in both deterministic and stochastic framework. In particular, we consider income, consumption and risky investment in the stochastic framework, extending previous results in \cite{Bayraktar2016}. The time cutoff m and n make the work more difficult. However, by establishing new controls,`\emph{quasi-ideal value}" and`\emph{ideal value}", we solve the corresponding ordinary differential equations or stochastic differential equations, and give the specific expressions for the maximum probability. Then we provide the optimal life insurance purchasing strategies and the optimal risk investment strategies. In general, when m \geqslant 0, n>0, deferred term insurance or term life insurance is a better choice for those who want to achieve their financial or bequest goals but are not financially sound. In particular, if m >0, n \rightarrow \infty, our viewpoint also sheds light on reaching a bequest goal by purchasing deferred whole life insurance. It is worth noting that when m=0, n \rightarrow \infty, our problem is equivalent to achieving the just mentioned bequest goal by purchasing whole life insurance, at which point the maximum probability and the life insurance purchasing strategies we provide are consistent with those in \cite{Bayraktar2014, Bayraktar2016}.

📄 PDF Abstract BibTeX arXiv:2301.04118

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

DNRSelect: Active Best View Selection for Deferred Neural Rendering

2025-01-21 · Dongli Wu, Haochen Li, Xiaobao Wei

Deferred neural rendering (DNR) is an emerging computer graphics pipeline designed for high-fidelity rendering and robotic perception. However, DNR heavily relies on datasets composed of numerous ray-traced images and de…

NeRFNeural Renderingreinforcement-learningReinforcement Learning

SelfGoal: Your Language Agents Already Know How to Achieve High-level Goals

2024-06-07 · Ruihan Yang, Jiangjie Chen, Yikai Zhang, Siyu Yuan 외

Language agents powered by large language models (LLMs) are increasingly valuable as decision-making tools in domains such as gaming and programming. However, these agents often face challenges in achieving high-level go…

Decision Making

The optimal investment strategy of a DC pension plan under deposit loan spread and the O-U process

2020-05-20 · Xiao Xu

This paper is devoted to invest an optimal investment strategy for a defined-contribution (DC) pension plan under the Ornstein-Uhlenbeck (O-U) process and the loan. By considering risk-free asset, a risky asset driven by…

Distributionally robust goal-reaching optimization in the presence of background risk

2021-08-10 · Yichun Chi, Zuo Quan Xu, Sheng Chao Zhuang

In this paper, we examine the effect of background risk on portfolio selection and optimal reinsurance design under the criterion of maximizing the probability of reaching a goal. Following the literature, we adopt depen…

When Agents Go Quiet: Output Generation Capacity and Format-Cost Separation for LLM Document Synthesis

2026-04-17 · Justice Owusu Agyemang, Michael Agyare, Miriam Kobbinah, Nathaniel Agbugblah 외 arxiv

LLM-powered coding agents suffer from a poorly understood failure mode we term output stalling: the agent silently produces empty responses when attempting to generate large, format-heavy documents. We present a theoreti…