Are Counterparty Arrangements in Reinsurance a Threat to Financial Stability?
Bibliographic record
Abstract
Interconnectedness among insurers and reinsurers at a global level is not well understood and may pose a significant risk to the sector, with implications for the macroeconomy. Models of the complex interactions among reinsurers and with other participants in the financial system and the real economy are at a very early stage of development. Parts of the market remain opaque to both regulators and market participants, particularly the counterparty arrangements among reinsurers through retrocession agreements. The authors create several plausible networks to model these relationships, each consistent with the financial statement data of the reinsurer. These networks are stress-tested under a series of severe but plausible catastrophic-loss scenarios. This analysis contributes to the literature by (i) applying a network-model approach common in the banking literature to the insurance industry; (ii) assessing the interconnections among reinsurers through potential claims rather than premiums; and (iii) investigating the most opaque part of the global insurance market, namely, counterparty arrangements among global reinsurers (retrocession). The authors find that contagion in the global reinsurance market is plausible and that the size of the potential market disruption is sensitive to (i) the distribution of risk among counterparties, (ii) the trigger for financial distress, (iii) the time horizon for claims resolution and (iv) the degree of loss netting. The findings suggest that further study of industry practices in these four areas would improve our ability to assess risk in the insurance sector and promote financial stability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".