Discerning the Impact of Derivatives on Asset Risk: The Case of Canadian Banks
Bibliographic record
Abstract
The Canadian banks have shown remarkable resilience to the financial crisis that intensified in the late 2008. The interesting question is whether this stability is due to their prudent lending practices to limit the original risk exposures or due to effective risk management through hedging by using financial derivatives. In this paper, we implement the option‐theoretic model of Merton to calculate the implied asset risk and discern the impact of these derivatives on the aggregate risk for Canadian banks over the period 1997–2008. An algorithm of iterative procedure is developed to impute asset value and risk from bank stock prices. Our estimates show that the risk for Canadian banks is low and even decreasing till the unfolding of the recent crises in 2008. Further analyses reveal that such low risks are not due to reliance on hedging, nor is it related to trading in derivatives, after disentangling the intertwined effects of hedging and trading. These results suggest that involvements in derivatives, in and of themselves, should not be blamed for causing the bank crises; rather, it is conservatism in controlling original risk exposures that remains fundamental for safeguarding a healthy financial system.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".