---
title: Who Keeps Your Debt Alive? Creditor Capacity and Corporate Debt Renewal
url: https://www.ml-quant.com/papers/ssrn/7668600/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-11-06
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 7668600
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7668600
featured: 2026-11-06
citations: unknown
topic: Risk, Credit & Banking
---


# Who Keeps Your Debt Alive? Creditor Capacity and Corporate Debt Renewal

A 2021 life-insurer capital rule change shows that when incumbent creditors' capacity shrinks, corporate debt issuance falls and firms struggle to refinance maturing debt.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7668600
- Identifier: SSRN 7668600
- Released: 2026-11-03
- First featured: Quant Letter No. 138 (2026-11-06): https://www.ml-quant.com/issues/2026-11-06/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Risk, Credit & Banking
- Authors: ZiHao Ji, Guan Wang, Mengchen Zhang, HongRu Zhang

## Related

- [Mean-field approximations in insurance](https://www.ml-quant.com/papers/arxiv/2511.04198/): A mean-field model simplifies complex insurance liabilities into manageable solutions, showing that large groups of interdependent individuals can be effectively analyzed in both life and non-life insurance scenarios.
- [Optimized Multi-Level Monte Carlo Parametrization and Antithetic Sampling for Nested Simulations](https://www.ml-quant.com/papers/arxiv/2510.18995/): Proposes an improved multilevel Monte Carlo with antithetic sampling to estimate loss probabilities and Value‑at‑Risk more efficiently for irregular payoffs, illustrated in life insurance.
- [Model Monitoring: A General Framework with an Application to Non-life Insurance Pricing](https://www.ml-quant.com/papers/arxiv/2510.04556/): The research explores concept drift in non-life insurance pricing, offering an overview of methods, performance metrics, and a monitoring process to determine when model adjustments are required.
- [An Interpretable Deep Learning Model for General Insurance Pricing](https://www.ml-quant.com/papers/arxiv/2509.08467/): The paper presents the Actuarial Neural Additive Model, a transparent deep learning model for insurance pricing that provides superior prediction accuracy and full transparency in its internal workings.
- [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.
- [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.