Peas in a pod: Canadian and Australian banks before and during a Global Financial Crisis
Bibliographic record
Abstract
In the aftermath of the Global Financial Crisis (GFC), the Canadian and Australian banking systems have been singled out by some commentators as having performed better than many other banking systems, particularly those in Europe, America and the United Kingdom.Banks in both Canada and Australia, for instance, have continued to report enviable earnings, sound capital levels, and high credit ratings both before and during the GFC.The G-20 and the European Union have tried to identify the features of the Canadian and Australian financial systems which have underpinned this success in order to use them in shaping a revised international regulatory framework.One area of focus has been the regulations governing "quality of capital".Despite these apparent successes, there is some evidence that both Canadian and Australian banks experienced considerable deterioration in the market value of their assets during the GFC.In this paper we use the KMV / Merton structural methodology, which incorporates market asset values, to examine default probabilities of 9 listed Canadian banks and 13 Australian listed banks in both a pre-GFC period (2000)(2001)(2002)(2003)(2004)(2005)(2006) and a GFC period (2007)(2008).We also modify the model to incorporate conditional probability of default which measures extreme credit risk.This paper finds that bank risk was significantly similar for Australian and Canadian Banks during the GFC period.This includes an assessment of impaired assets, Value at Risk (VaR) and Distance to Default (DD), as well as the extreme measures of Conditional VaR (CVaR), and Conditional Distance to Default (CDD); metrics which confirm the two countries similarities in terms of a significant increase in credit risk between pre-GFC and GFC periods.The extent of this increase was, however, far more pronounced for Australia, which was coming off a lower base.Bank risk for both countries was found to be far lower than for global counterparts due to factors such as sound regulatory control and low levels of involvement in sub-prime lending.This could provide lessons for global banks on risk management.A key conclusion of the paper is that it is important that fluctuating market values, especially the extreme fluctuations which are measured by CVaR and CDD, are a key consideration when determining risk management criteria such as capital adequacy.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".