An examination of large commercial banks within G-10 : risk, efficiency, and the 1996 market risk amendment
Bibliographic record
Abstract
The financial industry changed significantly through the 1990s as commercial banks pursued additional profits through non-traditional and off-balance sheet (OBS) activity. The regulatory bodies had to accept the changing risk nature of the industry and the response was the introduction of the 1996 Market Risk Amendment (MRA) by the Basle Committee. The MRA, through a series of 4 key announcements, was reached in January 1996 and fully implemented from January 1997, whereby banks were required to hold incremental capital to cover unexpected losses from market risk. In this study a multivariate regression model is used to investigate the effect of the MRA announcements on the returns to shareholders of commercial banks within G-10 countries (Belgium, Canada, France, Italy, Japan, the Netherlands, Sweden, Switzerland, the United Kingdom, and the United States). The empirical results suggest that bank shareholders in Italy, Sweden, and especially Japan benefited from the introduction of the MRA, while bank shareholders in the United Kingdom, Canada, and especially US experienced significant losses from 4 announcements that led to the final proposal of the 1996 MRA. The significant growth in income generated through OBS activities, such as trading and fee-based income, changed the risk profile financial institutions. This study employs four VaR methodologies (parametric, historical simulation, Monte Carlo simulation, and Extreme Value Theory) to calculate bank risk in a period of high 4 financial market volatility: 1992 through to 1998. The results show a strong increase in VaR for the years 1997 and 1998, with Japan showing the largest risk ranking over the period, while the US and Sweden were at the low end of the risk range. The VaR results indicate it may be misleading to compare risk scores across financial institutions if the reported numbers are based on different VaR methodologies. The results from the Monte Carlo (MC) and Extreme Value Theory (EVT) approaches result in the highest VaR estimates, while the parametric results were consistently lower. This thesis also employed Data Envelopment Analysis (DEA) to compute bank-level technical efficiency under Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS) between 1992 and 1998. The results for the entire bank sample across the period of study show inefficiency levels of 39% and 33%, under CRS and VRS respectively. The inclusion of off-balance sheet (OBS) activity in the DEA score is found to be significant, and indicate that the exclusion of this variable as an output leads to a misspecification and underestimation of bank efficiency. A Tobit regression approach was used to examine the relationship between bank efficiency and various bank and environmental variables. The second stage findings show that inflation is detrimental to bank efficiency, while a negative relationship is found between VaR and efficiency, indicating inefficient banks appear to take on less risk. A positive relationship is found between the MRA dummy variable and bank efficiency.
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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 | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".