Credit Portfolio Risk Evaluation based on the Pair Copula VaR Models
Why this work is in the frame
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Bibliographic record
Abstract
Aiming to solve the difficulty in describing the high dimensional dependency structure of credit assets, we construct pair copula VaR model to evaluate the credit portfolio risk. The empirical study which takes the publicly traded companies in Shanghai stock exchanges and Shenzhen stock exchanges shows that the Clayton copula with Canonical vine structure is the most appropriate function to describe the high dimensional low tail dependency structure. Meanwhile, the Monte Carlo simulation result proves that the pair copula VaR model can accurately measure the credit portfolio risk both in calm period and crisis period. Additionally, we acquired the optimal weights of the different credit assets in portfolio according to the simulation results of pair copula VaR models. Based on the research results, the commercial banks can dynamically adjust their credit asset allocation, so as to alleviate the credit portfolio risk and conduct more efficient credit risk management.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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