Bank systemic risk and the business cycle: An empirical investigation using Canadian data
Bibliographic record
Abstract
Since financial institutions are subjected to increasingly tighter requirements regarding the way they conduct their loan business, we could assume that built-in regulatory pressures induce them to adopt collective business strategies, with the unintended consequence of persistently weakening the banking system ability to cope with external shocks. Surprisingly, we find rather the opposite. This paper documents how banks, as a group, react to macroeconomic risk and uncertainty, and more specifically the way banks systemic behaviour evolves over the business cycle. Adopting the methodology of Beaudry et al. (2001), our results clearly indicate that the dispersion across banks traditional portfolios has actually increased through time. We introduce an estimation procedure based on EGARCH and re-fine Baum et al. (2002, 2004, 2009) and Quagliariello (2007, 2009) framework to analyze the question in the new industry context, i.e. shadow banking. Consistent with finance theory, we first confirm that banks tend to behave homogeneously vis-à-vis macroeconomic uncertainty. Additionally, we find that the cross-sectional dispersions of loans to assets and non-traditional activities shrink essentially during downturns, when the resilience of the banking system is at its lowest. Our results also indicate that banks herd-like behaviour remains predominantly a cyclical phenomenon, almost unaffected by the new banking environment. Most importantly however, the cross-sectional dispersion of market-oriented ac-tivities appears to be both more volatile and sensitive to the business cycle than the dispersion of the traditional banking business lines.
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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| 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) | 0.003 | 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".