Assessing Credit Risk in an Agricultural Loan Portfolio
Bibliographic record
Abstract
We show that agricultural lenders can implement a credit risk model that uses their loan portfolio data and complies with the new Basel Capital Accord without requiring Merton‐type model assumptions about underlying asset price volatility. A credit risk model is described and calibrated to the loan portfolio of a farm lender. The model is used to produce plausible estimates of expected loss, unexpected loss, and credit value‐at‐risk (VaR) at the portfolio and subportfolio (sector) levels. The lender could use these kinds of estimates to meet regulatory requirements or to adjust the level of capital in response to changing economic conditions. Nous montrons que les prêteurs agricoles peuvent appliquer un modèle de risque de crédit qui permet d'utiliser des données tirées de leur portefeuille de prêts et qui respecte le nouvel accord de Bâle sur les fonds propres, sans qu'il soit nécessaire d'utiliser les hypothèses du modèle de Merton sur la volatilité des prix des actifs. Nous avons décrit un modèle de risque de crédit et l'avons calibré en fonction du portefeuille de prêts d'un prêteur agricole. Le modèle est utilisé pour effectuer des estimations plausibles quant aux pertes prévues, aux pertes imprévues et à la valeur à risque au niveau du portefeuille et du sous‐portefeuille. Le prêteur pourrait utiliser ce genre d'estimations pour respecter les exigences réglementaires ou pour rajuster le niveau de fonds propres en fonction de l'évolution de la conjoncture économique.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".