Paper: arXiv 2602.08228

Authors: Alireza Ghahtarani, Ahmed Saif, Alireza Ghasemi

Abstract

Asset Liability Management (ALM) represents a fundamental challenge for financial institutions, particularly pension funds, which must navigate the tension between generating competitive investment returns and ensuring the solvency of long-term obligations. To address the limitations of traditional frameworks under uncertainty, this paper implements Distributionally Robust Optimization (DRO), an emergent paradigm that accounts for a broad spectrum of potential probability distributions. We propose and evaluate three distinct DRO formulations: mixture ambiguity sets with discrete scenarios, box ambiguity sets of discrete distribution functions, and Wasserstein metric ambiguity sets. Utilizing empirical data from the Canada Pension Plan (CPP), we conduct a comparative analysis of these models against traditional stochastic programming approaches. Our results demonstrate that DRO formulations, specifically those utilizing Wasserstein and box ambiguity sets, consistently outperform both mixture-based DRO and stochastic programming in terms of funding ratios and overall fund returns. These findings suggest that incorporating distributional robustness significantly enhances the resilience and performance of pension fund management strategies.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 6.0/10 (capped from 6.5: transaction costs not modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper provides a rigorous mathematical formulation comparing three distributionally robust optimization (DRO) ambiguity sets applied to asset-liability management. Empirical testing using real-world Canada Pension Plan (CPP) data demonstrates practical viability, although lack of explicit code links slightly limits full reproducibility.

Research Flowchart

  flowchart TD
    A[Research Goal: Enhance Pension Fund ALM<br/>via Distributionally Robust Optimization] --> B[Input Data: Empirical Data<br/>from Canada Pension Plan]
    B --> C[Implement 3 DRO Ambiguity Sets:<br/>Mixture, Box, Wasserstein]
    C --> D[Compare DRO Models against<br/>Traditional Stochastic Programming]
    D --> E[Key Finding: Wasserstein & Box DRO<br/>Outperform Mixture & Stochastic Models]
    E --> F[Outcome: Distributional Robustness<br/>Improves Solvency & Fund Returns]

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0 of 62 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.