Catastrophe Risk and Insurer Solvency:A Diffusion-Jump Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, the magnitudes of realized catastrophe (extreme-event) losses have increased dramatically. The effects of increasing catastrophe risks on the insurance industry have been profound. In the current private insurance market, the possibility of insurer default is of great concern to insurers and their investors. However, there is limited actuarial or financial theory for analyzing catastrophe insurance contracts based upon the probability of ruin. In this article, we develop a mixed diffusion and compound Poisson jump model of insurer net worth to reflect the fact that insurers are faced with both non-catastrophe and catastrophe risks. Under the assumption of exponentially distributed catastrophe losses, we derive analytical approximations to the insurer ruin probability. Assuming constant catastrophe loss amounts, we calculate the ruin probability numerically and compare the results with those for exponentially distributed losses.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it