Paper: arXiv 2508.13350

Authors: Raphael Chinchilla, Thomas D. Rueter, Timothy R. McDade, Peter R. Fisher, Emmanuel Candes, Trevor Hastie, Stephen Boyd

Abstract

This paper proposes a simulation-based framework for assessing and improving the performance of a pension fund management scheme. This framework is modular and allows the definition of customized performance metrics that are used to assess and iteratively improve asset and liability management policies. We illustrate our framework with a simple implementation that showcases the power of including adaptable features. We show that it is possible to dissipate longevity and volatility risks by permitting adaptability in asset allocation and payout levels. The numerical results show that by including a small amount of flexibility, there can be a substantial reduction in the cost to run the pension plan as well as a substantial decrease in the probability of defaulting.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper’s mathematics is moderately complex, involving stochastic simulations, optimization, and multi-period modeling, but lacks advanced derivations or dense formulas. Empirical rigor is high due to the detailed simulation framework, use of real-world data (pension fund liabilities, market returns), and concrete performance metrics like default probability and cost reduction.

Research Flowchart

  flowchart TD
  Goal["Research Goal: Assess & Improve Pension Fund Performance via Adaptability"] --> Inputs["Data/Inputs: Simulation Framework, Asset/Liability Data, Performance Metrics"]
  Inputs --> Meth["Methodology: Modular, Simulation-Based, Iterative Improvement"]
  Meth --> Comp["Computational Process: Simulate Asset Allocation & Payout Strategies"]
  Comp --> Find["Key Findings: Adaptability Reduces Default Risk & Costs"]
  Find --> Outcomes["Outcomes: Dissipates Longevity & Volatility Risks via Flexibility"]

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1 of 31 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.