A Nonparametric Test for Financial Contagion with Application to the Canadian Banking System
Bibliographic record
Abstract
This paper proposes a new test of financial contagion based on a nonparametric measure of the cross-market correlation. The test does not depend on the assumption that the data are drawn from a given probability distribution, therefore it allows for maximal flexibility in fitting into the data. Simulation studies show that our test has reasonable size and good power to detect financial contagion, and that the Forbes and Rigobon ’ test (2002) is conservative, suggesting that Forbes and Rigobon ’ test will tend not to find evidence of contagion when it does exist. The test is applied to investigate financial contagion of a variety of recent financial crises to the Canadian banking system. The empirical results reveal that: (i) compared to recent financial crises including the 1987 US stock market crash, 1994 Mexican Peso crisis, and 1997 East Asian Crisis, the ongoing 2007 subprime crisis has been having more persistent and stronger contagion impacts on the Canadian banking system; (ii) the October 1997 East Asian crisis induced contagion to Asian countries, and it quickly spread across Asian and to Latin America, and G7 countries. The contagious persistency of this crisis to the Canadian banking system was not as strong as the ongoing subprime crisis. However, it had a stronger impact on emerging markets; (iii) there was no evidence of contagion from the 1994 Mexican peso crisis to the Canadian banking system. Contagion occurred to Argentina, Brazil, and Chile, but it was only constrained in the region of Latin America.
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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 | 0.008 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".