Modeling and measuring insurance risks for a hierarchical copula model considering IFRS 17 framework
Bibliographic record
Abstract
In this thesis, a stochastic approach to insurance risk modeling and measurement that is compliant with the new International Financial Reporting Standards (IFRS 17) is proposed. The compliance is achieved through the use of a semiparametric hierarchical copula which accounts for the dependence between the lines of business of the Canadian auto insurance industry. A model for the marginal unpaid claim liabilities of each line of business based on double generalized linear models is also developed. Development year and accident year effect factors along with an autoregressive feature for residuals enable modeling the dependence between the various entries of the loss triangles in a given line of business. Capital requirements calculations are then performed through simulation; numbers obtained with univariate and multivariate risk measures are compared. Moreover, a risk adjustment for non-financial risk required by IFRS 17 is also computed through a cost of capital approach.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".