Identifying Systemically Important Banks in Pakistan: A Quantile Regression Analysis
Bibliographic record
Abstract
The basic purpose of this study is to identify the systemically important banks of Pakistan using an unbalanced panel dataset of 21 commercial banks. The study covers the period 2004-2014. The systemic risk of financial institutes is calculated by using Conditional Value at Risk (CoVaR) approach. Specifically, first, the VaR and CoVaR are obtained as predicted values of quantile regression of individual and market losses. The state variables included in the analysis are the change in three month yield of treasury bills, the change in slope of yield curve, the inflation rate, monthly market returns, and the equity volatility. The study shows that the state variables have significant impacts on the CoVaR of financial institutions. The results of the study helps policy makers and regulatory authorities to revise policies and regulation by keeping in mind systemically important banks in the economy to reduce the chances of a financial debacle in future in Pakistan and thus rescuing the economy from financial crises.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".