quant research // summarizing the paperLOADING
Debora Daniela Escobar, Wing Fung Chong · 2026-06-22
The paper studies how multiple policyholders can transfer risk to a single central insurer when prices are set endogenously by pricing rules applied to the risks ceded. It shows that standard Pareto-optimality can hide the fact that some participants get effectively ignored (assigned zero weight), and introduces a refined criterion — 'inclusive and fair Pareto optimality' — that requires every agent to be explicitly represented exactly once in an ordered sequence of optimizations. Its main theoretical result proves this refinement is equivalent to a balanced sequential optimization procedure and sits between Geoffrion-proper and classical Pareto optimality.
Why it matters: For those working on insurance design, reinsurance, or pooled/centralized risk-sharing arrangements, this offers a formal way to ensure every participant's risk position is genuinely accounted for rather than washed out by aggregate optimization. It could inform fairness and inclusivity considerations in structuring indemnity contracts and pricing, though it is a conceptual/theoretical contribution rather than a trading tool.
⚠ This is an abstract theoretical framework illustrated only by an example, with no empirical validation or direct application to market trading.
This paper studies centralized risk sharing with endogenous prices. Multiple policyholders transfer risks to a central insurer through indemnity decisions, while prices are determined by pricing functionals applied to ceded risks. The resulting problem is multiobjective, with Pareto optimality as the natural efficiency criterion. We show that classical Pareto optimality may fail to reveal whether all agents are represented in a balanced decision process that scalarized objectives may assign zero weight to some agents, and group aggregates may obscure individual risk positions. Motivated by bilateral Pareto characterizations through sequential optimization, we introduce inclusive and fair Pareto optimality, a representation-based refinement requiring every agent to appear exactly once, either individually or as part of a group, in a finite ordered sequence of optimizations. Our main result proves equivalence between this concept and balanced sequential optimization, placing it between Geoffrion-proper Pareto optimality and classical Pareto optimality. An illustrative example demonstrates the framework using the Expected Shortfall.
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AI summary generated from the paper’s public abstract via arXiv; it may miss nuance — read the source before relying on it. Thank you to arXiv for its open-access interoperability; StockTools is not affiliated with arXiv, and all rights remain with the authors. Educational only, not financial advice.