Detecting shift-contagion in currency and bond markets
Bibliographic record
Abstract
It is well known that equity, currency, or banking crises generate substantial real costs for the country in which they occur. Authorities and financial market participants have often been concerned that these crises would spill over or spread, leading to financial system volatility or crises elsewhere in the world. The recent Mexican, Asian, and Russian crises are examples where shocks originating in one country are believed to have spread to other nations and to have resulted in significant costs to the international community. The transmission of crises from one country to another (or from one market to another) is loosely referred to as contagion, but its definitions are many. One is that contagion occurs when the propagation of shocks is in excess of fundamentals, that is, when shocks have an impact beyond the amount channelled through the usual commercial, financial, and institutional ties between markets. Another, more narrow description is that contagion occurs when shocks spread through herding or irrational behaviour. In contrast, a third and much broader definition refers to contagion as the transmission of shocks through any channels that cause markets to co-vary. There is now a fourth and more precise definition, referred to as “shift contagion, ” which suggests that contagion occurs when the propagation of shocks during crisis periods increases systematically from that observed during normal times. Given this multiplicity of definitions, it is not surprising to find widely * The complete version of this paper will be published as a Bank of Canada working paper. The electronic version will be available on the Bank of Canada Web site.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".