Impact of COVID-19, Political, and Financial Events on the Performance of Commercial Banking Sector
Bibliographic record
Abstract
This paper employs a structural empirical model to gauge the possible effects of COVID-19, political and financial events on the returns and volatility of commercial banks. It observes that insured and run-prone uninsured depositors choose between differentiated commercial banks, which appears to be significantly impacted from the present pandemic, especially for the case of Pakistan’s commercial banking sector. The estimated volatility series for commercial banks is measured through the GARCH model, which explains the current financial and political distress for the case of shocks from COVID-19. We calibrate by Impulse Indicator Saturation (IIS) to detect the structural breaks formed by these events in the returns and volatility series of commercial banks. It is observed that the calibrated model possesses almost all financial events that have had a prominent impact on the returns and volatility series whereas two out of eighteen political events are unimpacted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".