Data for the spatiotemporal analysis of US global banks’ exposure to foreign counterparty risks
Bibliographic record
Abstract
This article Presents an extract of the fully consolidated data collected through the US Federal Financial Institutions Examination Council (FFIEC) reports, FFIEC 009 and FFIEC 009a [1]. The data is provided here as a Panel of Quarterly claims covering the 2017 fiscal year from last quarter 2016, to third quarter 2017. Following U.S. generally Accepted Accounting Principles (GAAP), it contains financial claims reported by 68 US banking organizations (including US holding companies owned by foreign banks, but excludes US branches of foreign banks), on foreign counterparties distributed across 71 countries in six world regions. From the original raw claims data we generate and include in this shared data, six accounting measures of foreign country risks (including Cross-border risk ratio, Foreign Office risk ratio, Derivative risk ratio, Ratio of Public Sector Claims, Ratio of Banking Sector Claims, and Ratio of non-bank financial sector claims) previously used to study the relative contribution of public sector, banking sector, and non-bank financial sector claims in U.S. global banks' exposure to foreign counterparties' default risks in [2]. The present article also presents a brief descriptive analysis of the various measures and their inter-relationships in characterizing US global banks exposures to foreign counterparties risks.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".