Credit Spreads Between German and Italian Sovereign Bonds: Do One‐Factor Affine Models Work?
Bibliographic record
Abstract
Abstract In this paper we analyze the credit spread between Italian and German government bonds after the exchange‐rate agreement in May 1998. We estimate the parameters of two mean‐reverting affine models for the German term structure and the spread process—the Gaussian Vasicek and the square‐root Cox‐Ingersoll‐Ross (CIR) model. Similar to Pearson and Sun (1994) we combine cross‐sectional and time‐series information of daily observations to estimate the process parameters employing a maximum likelihood method. Our empirical results show that the Vasicek and CIR model describe the German term structure dynamics equally well. Both models fail to account for all observed shapes of the credit spread structure whereas the spread residuals in the Vasicek case seem to be less volatile. Our results suggest application in the area of pricing credit‐sensitive instruments such as credit derivatives or the management of credit risk, especially for European government debt. Résumé Nous analysons l'étalement du crédit entre les obligations d'état italiennes et allemandes après l'accord sur le taux d'intérět de mai 1998. Nous évaluons les paramètres de deux modèles de retour à la moyenne pour la structure échéancière allemande et le processus d'étalement—le Gaussian Vasicek et le modèle racine‐carrée Cox‐Ingersoll‐Ross (CIR). Similairement à Pearson et Sun (1994) nous combinons l'information échantillonnée et en série d'observations quotidiennes afin d'évaluer les paramètres employant une méthode à probabilité maximum. Nos résultats empiriques démontrent que les modèles Vasicek et CIR sont incapables de considérer toutes les formes observées de la structure d'étalement du crédit tandis que les soldes étalés dans le cas Vasicek semblent moins volatiles. Nos résultats suggèrent une application dans le domaine de la valorisation d'instruments à crédit instable tels que les dérivés de crédit ou la gestion des risques de crédit, spécifiquement pour la dette gouvernementale européenne.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".