The change in banks' product mix, diversification and performance: An application of multivariate GARCH to Canadian data
Bibliographic record
Abstract
Data suggest a change in banks’ performance attributable to a greater involvement in non-traditional activities. Indeed, market-oriented banking increases banks’ accounting returns at the cost of a higher volatility in financial results. The motivation of this paper is to study how bank product mix impacts diversification and performance. Thanks to our data and methodology we are able to shed new light on the apparently contradictory results found in the literature regarding the benefits to diversify in market-based banking. Some keys conditional volatilities reveal these benefits may in fact vary both over the business cycles and through time. Using a new framework based on a multivariate GARCH procedure and a modified Hausman test, our main findings suggest that most components of non-interest income actually provide non-negligible diversification benefits with respect to traditional banks’ business lines. In normal times, diversification even works for the components most related to market-oriented banking, i.e., trading income and capital markets fees. Not so surprisingly however, during crisis episodes these diversification benefits seem to vanish for most of the components, except for insurance and securitization, which act as buffers. Consistent with the literature, we also find that, despite the evolution of the banking business model, fees related to banks’ traditional activities – deposit, credit card and loan fees – remain the most stable and profitable sources of income.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".