A cost benefit analysis of operational risk quantification methods for regulatory capital

Master Thesis


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University of Cape Town

Operational risk has attracted a sizeable amount of attention in recent years as a result of massive operational losses that headlined financial markets across the world. The operational risk losses have been on the back of litigation cases and regulatory fines, some of which originated from the 2008 global financial crisis. As a result it is compulsory for financial institutions to reserve capital for the operational risk exposures inherent in their business activities. Local financial institutions are free to use any of the following operational risk capital estimation methods: Advanced Measurement Approach (AMA), the Standardized (TSA) and/ the Basic Indicator Approach (BIA). The BIA and TSA are predetermined by the Reserve Bank, whilst AMA relies on internally generated methodologies. Estimation approaches employed in this study were initially introduced by the BCBS, largely premised on an increasingly sophisticated technique to incentivise banks to continually advance their management and measurement methods while benefiting from a lower capital charge through gradating from the least to the most sophisticated measurement tool. However, in contrast to BCBS's premise, Sundmacher (2007), whilst using a hypothetical example, finds that depending on a financial institution's distribution of its Gross Income, the incentive to move from BIA to TSA is nonexistent or marginal at best. In this thesis I extend Sundmacher (2007)'s work, and I test one instance of AMA regulatory capital (RegCap) against that of TSA in a bid to crystalise the rand benefit that financial institutions stand to attain (if at all) should they move from TSA to AMA. A Loss Distribution Approach (LDA), coupled with a Monte Carlo simulation, were used in modelling AMA. In modelling the loss severities, the Lognormal, Weibull, Burr, Generalized Pareto, Pareto and Gamma distributions were considered, whilst the Poisson distribution was used for modelling operational loss frequency. The Kolmogorov-Smirnov and Akaike information criterion tests were respectively used for assessing the level of distribution fit and for model selection. The robustness and stability of the model were gauged using stress testing and bootstrap. The TSA modelling design involved using predetermined beta values for different business lines specified by the BCBS. The findings show that the Lognormal and Burr distributions best describes the empirical data. Additionally, there is a substantial incentive in terms of the rand benefit of migrating from TSA to AMA in estimating operational risk capital. The initial benefit could be directed towards changes in information technology systems in order to effect the change from TSA to AMA. Notwithstanding that the data set used in this thesis is restricted to just one of the "big four banks" (owing to proprietary restrictions), the methodology is representable (or generalisable) to the other big banks within South Africa. The scope of this study can further be extended to cover Extreme Value Theory, Non-Parametric Empirical Sampling, Markov Chain Monte Carlo, and Bayesian Approaches in estimating operational risk capital.