Paper: arXiv 2508.05241

Authors: Wanting He, Wenyuan Li, Yunran Wei

Abstract

This paper introduces an innovative framework for the periodic evaluation of defined-contribution pension funds. The performance of the pension fund is evaluated not only at retirement, but also within the interim periods. In contrast to the traditional literature, we set the dynamic risk measure as the criterion and manage the tail risk of the pension fund dynamically. To effectively interact with the stochastic environment, a model-free reinforcement learning algorithm is proposed to search for optimal investment and insurance strategies. Using U.S. data, we calibrate pension members’ mortality rates and enhance mortality projections through a Lee-Carter model. Our numerical results indicate that periodic evaluations lead to more risk-averse strategies, while mortality improvements encourage more risk-seeking behaviors.

Complexity vs Empirical Score

  • Math Complexity: 8.0/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper provides a strong theoretical foundation using dynamic risk measures and time-consistent dynamic programming equations combined with reinforcement learning. Its empirical application to US mortality data via an enhanced Lee-Carter model offers meaningful and novel practical insights into defined-contribution pension management.

Research Flowchart

  flowchart TD
    A[Research Goal: Periodic Evaluation & Dynamic Risk Management of DC Pension Funds] --> B[Data Inputs: US Mortality Data & Lee-Carter Projection Model]
    B --> C[Framework: Formulate Dynamic Risk Measure for Interim & Retirement Evaluation]
    C --> D[Optimization: Model-Free Reinforcement Learning for Investment & Insurance Strategies]
    D --> E[Outcome 1: Periodic Evaluations Lead to More Risk-Averse Strategies]
    D --> F[Outcome 2: Mortality Improvements Encourage More Risk-Seeking Behaviors]

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2 of 78 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.