Bibliographic record
Abstract
The recent financial crisis started a global debate on the role of financial policies, which led to financial system reforms in many countries. These reforms mainly consisted of increasing the usage of macroprudential policies. This dissertation seeks to understand whether macroprudential policies in financially integrated countries reduced their vulnerability to the impact of external shocks. Chapter 2 empirically examines the impact of macroprudential policies on cross-border bilateral credit growth. Capital requirements and loan-to-value (LTV) ratios, in 15 lending countries and 34 borrowing countries between 2000 and 2014, are used in the analysis. The results show that in some countries, the increase of capital requirements is not effective in reducing international credit flows during periods of financial vulnerability. The impact of tightening LTV ratios is more heterogeneous across countries because LTV ratios are mainly used in the housing sector and not all countries change their LTV ratio frequently. Hence, cooperation across countries is necessary but also countries should make sure that the change of macroprudential policies targeting lenders and those targeting borrowers complement each other to avoid international leakages. Chapter 3 analyzes issues related to the international spillover of macroprudential policies through international banking activities using a two-country dynamic stochastic general equilibrium model with heterogeneous and time-varying macroprudential policies. The results show that a combination of capital requirements and LTV ratios is effective in reducing credit growth despite the existence of cross-border banking activities and heterogeneous implementation of capital requirements across countries. In addition, international coordination of capital requirements is also effective in reducing credit growth but less effective than a combination of capital requirements and LTV ratios. Chapter 4 focuses on the role of countercyclical LTV ratios in reducing transmission of shocks when international investors, holding domestic and foreign assets, face collateral constraint. Using a two-country dynamic stochastic general equilibrium model, the analysis demonstrates that time-varying LTV ratios can reduce the transmission of shocks.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".