An examination of large commercial banks within G-10 : risk, efficiency, and the 1996 market risk amendment
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».