Explore the Optimal Investment-Reinsurance Strategy for Chinese Insurance Companies Under Catastrophe Risks
Bibliographic record
Abstract
The Catastrophe Risk Management System is a complex, multi-layered structure that involves various entities, including insurance companies. Insurance industry participation is crucial to the system’s success, but massive compensation claims can lead to heavy financial burdens and even bankruptcy for insurers. To address this challenge, this paper explores an optimal investment and reinsurance strategy for insurers to manage their exposure to catastrophe risks. To achieve this, we develop a surplus process for insurance businesses that incorporates interference items. We then apply the Hamilton-Jacobi-Bellman (HJB) equation to identify the optimal investment and reinsurance strategy that minimizes the risk of financial ruin. Furthermore, we perform a sensitivity analysis on the factors that may affect the optimal investment and reinsurance strategy. Our research provides valuable insights for insurance companies to improve their participation in catastrophe management. With the optimal investment and reinsurance strategy suggested in this study, insurance companies can reduce their risk exposure and increase their investment returns, thereby enhancing their ability to manage catastrophic events.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".