---
title: Ambiguity in Insurance
url: https://www.ml-quant.com/papers/ssrn/5250219/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 5250219
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5250219
featured: 2025-05-14
citations: unknown
topic: Risk, Credit & Banking
---


# Ambiguity in Insurance

Price movements in catastrophe bonds can be predicted by ambiguity preference in economic outlook and natural disasters, especially during crises and geopolitical conflicts.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5250219
- Identifier: SSRN 5250219
- Released: 2025-05-11
- First featured: Quant Letter No. 97 (2025-05-14): https://www.ml-quant.com/issues/2025-05-14/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Risk, Credit & Banking

## Related

- [Systemic Risk in the European Insurance Sector](https://www.ml-quant.com/papers/arxiv/2505.02635/): The study investigates the relationship between the European insurance sector and financial markets, finding that the insurance market contributes to systemic risk, especially during financial crises.
- [Marginal Fairness: Fair Decision-Making under Risk Measures](https://www.ml-quant.com/papers/arxiv/2505.18895/): The article introduces a concept of marginal fairness for unbiased decision-making in sectors like insurance and finance, disregarding protected attributes such as race, gender, and religion.
- [Balancing Profit and Fairness in Risk-Based Pricing Markets](https://www.ml-quant.com/papers/arxiv/2506.00140/): The study introduces a new tax schedule and an open-source simulator, MarketSim, aimed at improving fairness in markets like health insurance and consumer credit by aligning private incentives with social objectives.
- [Dynamic Reinsurance Treaty Bidding via Multi-Agent Reinforcement Learning](https://www.ml-quant.com/papers/arxiv/2506.13113/): The paper introduces a multi-agent reinforcement learning framework for reinsurance treaty bidding, showing its ability to enhance risk transfer efficiency and surpass traditional pricing methods in reinsurance markets.
- [Benchmark-Neutral Risk-Minimization for insurance products and nonreplicable claims](https://www.ml-quant.com/papers/arxiv/2506.19494/): The research investigates the pricing and hedging of nonreplicable contingent claims like long-term insurance contracts using a benchmark-neutral pricing framework, suggesting an algorithmic refinancing strategy for working capital modeling.
- [Decoding Financial Health in Kenyas' Medical Insurance Sector: A Data-Driven Cluster Analysis](https://www.ml-quant.com/papers/arxiv/2502.17072/): A study using advanced clustering techniques emphasizes the need for transparency and timely reporting in the financial performance of medical sector insurance companies.
