Measuring the systemic risk in indirect financial networks
Why this work is in the frame
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Bibliographic record
Abstract
In this study, we present a novel measurement approach for systemic risk by considering an indirect network structure. In a departure from previous studies, this measurement method captures spillovers arising from deleveraging and price impact in financial systems and calculates the amplification of losses during the contagion process. We show the relationship between a bank's vulnerability and its network connections. Applying the model to Chinese banks, we evaluate the fire-sale loss of each bank and quantify the impact of each asset in different simulated stress scenarios. Using both theoretical and empirical evidence, we show the ability of network centrality to explain systemic risk contribution: a bank with more network connections is systemically more important. We also present an optimal strategy to mitigate and govern systemic risk. Our result implies that the systemic importance of a bank is based not only on its size but also on the kinds of assets it holds; it provides useful systemic risk monitoring tools complementary to those currently used by supervisors.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it