Does Trading in Derivatives Affect Bank Risk? The Canadian Evidence
Bibliographic record
Abstract
We delineate the impact of derivatives trading on asset risk for Canadian banks over the period starting 1997 till the fallout of the bank crisis in 2007. In light of the remarkable resilience of Canadian banks in dodging the current financial turmoil, we investigate whether such bank stability is attributable to effective risk management through derivatives use. After imputing asset risk from bank stock prices based on the option-theoretic model of Merton (1974), we ascertain the links between the implied asset risk and derivatives use for trading and hedging purposes. Our findings reveal that not only bank risk increases with trading in derivatives, but increases also with derivatives reportedly used for hedging. This puzzling evidence is robust to different model specifications and alternative methods of estimations. Our new evidence is important in two ways. First, it casts doubt on the effectiveness of hedge accounting. Second, it shows that the use of derivatives by Canadian banks does not explain their envied soundness. We therefore conclude that prudent practices limiting original risk exposures remain fundamental for safeguarding a healthy financial system. This lesson from Canada is particularly relevant for China, given its developing financial infrastructure and extreme reliance on banks in providing financing to its economy.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".