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Record W7099030680

Banking Crises, Contagion, and Foreign- Asset Exposures of Canadian Banks

2004· article· en· W7099030680 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicColeoptera Taxonomy and Distribution
Canadian institutionsnot available
Fundersnot available
KeywordsAsset (computer security)Panel dataOrder (exchange)Foreign exchangePrivate sector
DOInot available

Abstract

fetched live from OpenAlex

Do not cite without permission. The objective of this paper is to address these two issues: first, to what e xtent have Canadian banks become increasingly “globalized”; and second, do Canadian banks ’ foreign asset exposures respond to contagious crisis events? In order to describe the behaviour of the foreign asset exposures of Canadian banks, and to assess the existence and impact of contagion, firm-level panel data on Canadian banks ’ is employed. This unique Bank of Canada data set extends from 1984 to 2003 on a quarterly basis for a set of Canadian banks with claims in over 160 foreign jurisdictions. Specifically, banks ’ foreign asset exposures include loans and deposits to foreign firms, banks, and public sector entities, and holdings of public and private securities. The panel nature of the data permits tests of the existence of informational based contag ion and for its possible impact on the foreign asset portfolios of Canadian banks. Specifically, do banks reduce their foreign claims to countries that appear to be similar to countries that have experienced a banking crisis? Preliminary results find that, conditional on fundamentals, banks do not adjust their portfolios immediately in the presence of crisis events. Thus, informational contagion plays only a small role in determining banks ’ asset portfolios.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.990
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.026
GPT teacher head0.201
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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