The Effect of Using Securitization on the Stability and the Risk of Banks: Evidence From Emerging Countries
Bibliographic record
Abstract
The purpose of this study is to investigate whether securitization affect financial stability and risk of banks issued from emerging countries during the period 2007 to 2017.To reach this end we conduct dynamic panel data econometrics with Generalized Methods of Moments (GMM) system on 20 banks issued from emerging countries. The dependent variables are defined by “Bank Stability Index” (BSI) and “logarithm of z-score” and ratios of total risk and credit risk. The independent variables are split into variable of interest (securitization ratio), bank-specific variables (capital adequacy, profitability, on-balance sheet interest rate risk, financial margin, income diversification, liquidity and bank size) and country-specific variables (GDP and inflation).As major conclusion, we find that using securitization - by banks from emerging countries – enhances their financial stability and minimizes their total risk and credit risk.As a practical contribution to this work, we suggest that banks' decision-makers in emerging countries increase their use of securitization in order to benefit from its beneficial effect on their financial stability and risks.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".