Pricing Dynamics and Solvency in Insurance: Capital Allocation, Surplus and Insurance Cycle
Bibliographic record
Abstract
Abstract This paper proposes a stochastic multi-period pricing model based on the default option value with insurance cycle to examine the interactions among pricing, surplus allocation and solvency for a multiline insurer. The proposed innovative model captures the dynamic aspects of capitalization and the impact of dynamic premium setting on the insurer’s solvency and risk management. We derived the equilibrium premium for different insurance contract designs. Our results show that the allocation of surplus per line affects the default of the other lines and depends on the correlation between the solvency ratio and the loss ratio of the line. The presence of the insurance cycle can boost solvency provided that the insurer adopts the right underwriting strategy. Based on the correlation between the solvency ratio and the loss ratio of the line, the insurer can make strategic decision about fair pricing. This makes it possible to reconcile the objectives of pricing with the insurer’s solvency and its strategic decision-making in a long-term perspective.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".