How Do Canadian Banks That Deal in Foreign Exchange Hedge Their Exposure to Risk?
Bibliographic record
Abstract
This paper examines the daily hedging and risk-management practices of financial intermediaries in the Canadian foreign exchange (FX) market. Results reported in this paper suggest that financial institutions behave similarly when managing their market risk exposure. In particular, dealing banks do not fully hedge their spot market risk. The results reported support arguments by Stulz (1996) and Froot and Stein (1998) that the amount of hedging will depend on a firm's comparative advantage in bearing risk. While the extent of hedging is found to depend on market volatility and the magnitude of their risk exposure, the uniqueness of the dataset employed in this paper allows for an explicit test of the various sources of comparative advantage that dealing banks in the FX markets have in their role as market-makers. Private information via customer order flow, guaranteed access to liquidity, and the capital-allocation structure of a dealer's financial institution are potential sources of comparative advantage to dealing banks in the FX market. A model with private information and an imperfectly competitive environment is provided to illustrate hedging when informed agents in a multiple security market behave strategically. Empirical results suggest that dealing banks only selectively hedge speculative positions taken in the spot market in the forward market. Findings also suggest that dealing banks share in the risk exposure of the spot market's net position without simultaneously hedging this risk.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".