The Impact of Contagion on Non-Performing Loans: Evidence from Australia and Canada
Bibliographic record
Abstract
Despite Canadian and Australian banks being widely perceived as having weathered the storm of the Global Financial Crisis (GFC) very successfully, the impaired assets (also known as non-performing loans) of both these two countries increased several fold during this crisis. Previous studies in other countries have tended to focus on the impact of bank specific factors, such as size and return on equity, in explaining bank risk. Our approach involves including those traditional variables, plus Distance to Default (DD), and a novel contagion variable, which is the effect of major global bank DD on Australian and Canadian non-performing loans. The study incorporates all twenty two listed Australian and Canadian Banks and uses a fixed effects panel data regression over the period 1999-2008. Robustness checks include correlation and VIF analysis as well a two stage least squares model as an alternative. We find that bank specific balance sheet and income statement factors are not good explanatory variables for bank risk. In contrast, the contagion variable is significant in explaining Canadian and Australian bank risk, which suggests that prudential regulators should look to specifically allocate a portion of regulatory capital to deal with contagion effects.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".