Bibliographic record
Abstract
A growing share of financial transactions takes place outside the scope of financial regulation. The mortgage market is a specific example of this phenomenon. Some mortgage lenders do not face the same level of scrutiny as chartered banks or credit unions. These non-regulated financial intermediaries do not have to maintain a minimum level of bank capital (or equity) and cannot accept deposits. To finance their lending, they rely on other sources of funds such as securitization. In this thesis, I examine the impact of non-regulated mortgage lenders on the macroeconomy. In the first chapter, I present evidence on rates in the Canadian mortgage market where traditional lenders facing a minimum capital requirement compete against non-regulated financial intermediaries. My results show that non-regulated lenders offer lower mortgage rates. Differences in the type of household they serve could explain this observation. In Chapter 2, I build a dynamic and stochastic general equilibrium (DSGE) model to examine the impact of non-regulated financial intermediaries on the economy. My model features two types of financial intermediaries that differ in three ways: (i-) only the regulated sector faces a capital requirement, (ii-) the non-regulated sector cannot accept deposits, and (iii-) the non-regulated sector faces a more elastic demand. I also analyze the effectiveness of three regulatory tools aimed at improving macroeconomic and financial stability (often referred to as macroprudential tools). The simulations show that a larger non-regulated sector contributes to stabilizing the economy and has a non-trivial impact on the effectiveness of different macroprudential tools. Finally, in Chapter 3, I examine the impact of non-regulated financial intermediaries on optimal monetary policy. Mortgage rates are traditionally an important channel for the transmission of monetary policy and the expansion of non-regulated lenders could interfere with this mechanism. My results suggest that a trade-off exists between minimizing the volatility of output and inflation and implementing a monetary policy rule that is robust to the growth of the non-regulated sector.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".