Splitting Credit Risk into Systemic, Sectorial and Idiosyncratic Components
Bibliographic record
Abstract
We provide a methodology to estimate a global credit risk factor from credit default swap (CDS) spreads that can be very useful for risk management. The global risk factor (GRF) reproduces quite well the different episodes that have affected the credit market over the sample period. It is highly correlated with standard credit indices, but it contains much higher explanatory power for fluctuations in CDS spreads across sectors than the credit indices themselves. The additional information content over iTraxx seems to be related to some financial interest rates. We first use the estimated GRF to analyze the extent to which the eleven sectors we consider are systemic. After that, we use it to split the credit risk of individual firms into systemic, sectorial, and idiosyncratic components, and we perform some analyses to test that the estimated idiosyncratic components are actually firm-specific. The systemic and sectorial components explain around 65% of credit risk in the European industrial and financial sectors and 50% in the North American sectors, while 35% and 50% of risk, respectively, is of an idiosyncratic nature. Thus, there is a significant margin for portfolio diversification. We also show that our decomposition allows us to identify those firms whose credit would be harder to hedge. We end up analyzing the relationship between the estimated components of risk and some synthetic risk factors, in order to learn about the different nature of the credit risk components.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".