Bibliographic record
Abstract
The 2007 global financial crisis brought sharply into focus the need for macroprudential policy as a means of controlling systemic financial stability. This has become a focal point for policy-makers and numerous central banks, including the Bank of Canada, but it has its drawbacks, particularly here in Canada. As a counterbalance to microprudential policy, the idea of a macroprudential outlook reaches beyond the notion that as long as every banking institution is healthy, financial stability is assured. Macroprudential policy recognizes that all those financial institutions are linked, and that stability at the individual level may translate to fragility and uncertainty at the macro level. There are two approaches to macroprudential policy, and both come with downsides. One approach examines the network factor, in which banks are linked through their inter-connected financial transactions. A domino effect can thus be created; when one bank defaults, it causes a chain reaction down the line, creating instability in other banks in the network. The extent of this contagion of instability can be clearly observed through this model; unfortunately, it requires the use of detailed information typically available only to a limited circle of bank supervisors. The second approach gleans information from bank stock prices in a poorly performing market. This information is easily available and accessed, but the downside is the lack of clear understanding on how exactly these shocks travel through the complex links of the global banking system. Canada’s banking system is small and has only six major banks. However, it is important to understand how they are interconnected and how each individual bank can contribute to overall risk. Not only do banks need to be sufficiently capitalized in the normal business cycle, but it may be worthwhile for the sake of overall financial stability to create mechanisms, as regulators in some countries are doing, that require banks to hold more capital in good economic times so that they can use it as a buffer in case of a downturn. Another important macroprudential tool is to identify how much each bank contributes to systemic risk. This would entail identifying the banks that pose a greater threat to stability and having them hold extra capital. Assigning proper capital requirements is, however, not as straightforward as it may seem as the risk of the banking system changes when capital requirements change. One study has shown that when properly done such a requirement can reduce by one-quarter the probability of a financial crisis. Implementing macroprudential policy in Canada faces some challenges. With both housing prices and the level of Canadians’ personal debt high, sudden corrections to the financial system can create problems. Also, the interconnections between Canadian and foreign banks could result in the former being much more greatly influenced by financial-crisis spillover from the latter, something Canada generally avoided during the 2007 economic meltdown. There’s no consensus as yet on the objectives of macroprudential policy. However, it is a necessary complement to microprudential policy and provides a means of managing systemic risk with the goal of greater global financial stability.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".