Basel II provides banks with four options that may be used to calculate regulatory capital for operational risk. Each of these options (except the most basic approach) requires an underlying risk measurement and management system, with increasing complexity and more refined capital calculations under the more advanced approaches. Approaches available are BIA, TSA, ASA and AMA. The most advanced and complex option under Basel II is the AMA. This approach allows a bank to calculate its regulatory and economic capital requirements (using internal models) based on internal risk variables and profiles, rather than exposure proxies like gross income. This is the only risk–sensitive approach allowed by and described in Basel II. Accompanying internal models, complex and sophisticated measurement instruments, risk management processes and frameworks, as well as a robust governance structure need to be implemented. This study focuses on the practical design and implementation of an AMA capital model. This includes a beginning–to–end solution for capital modelling and covers all elements of data analysis, capital calculation and capital allocation. The proposed capital model is completely risk–based, leading to risk–sensitive capital calculations and allocations for all business lines in a bank. The model was constructed to comply fully with all Basel II requirements and standards. The proposed model was subsequently applied to one South African bank’s operational risk data, i.e. risk scenario and internal loss data of the bank were used as inputs into the proposed capital model. Regulatory capital requirements were calculated for all business lines in the bank and for the bank as a whole on a group level. Total capital requirements were also allocated to all business lines in the bank. For regulatory capital purposes, this equated to the stand–alone capital requirement of each business line. Calculations excluded the modelling and incorporation of insurance, expected loss offsets and correlation. These capital mitigation techniques were, however, proposed as part of the comprehensive capital model. AMA based capital calculations for the bank’s business lines resulted in significant capital movements compared to TSA capital requirements for the same calculation periods. The retail banking business line was allocated less capital compared to corresponding TSA estimates. This is mainly attributable to lower levels of tail risk exposure given high income levels (which are the bases for TSA capital calculations). AMA–based capital for the investment banking business line was higher than corresponding TSA estimates, due to high levels of extreme risk exposure relative to income generated. Employing capital modelling results in operational risk management and performance measurement was discussed and proposals made. This included the use of capital requirements (modelling results) in day–to–day operational risk management and in strategic decision making processes and strategic risk management. Proposals were also made on how to use modelling results and capital allocations in performance measurement. It was proposed that operational risk capital costs should be included in risk–adjusted performance measures, which can in turn be linked to remuneration principles and processes. Ultimately this would incentivise sound operational risk management practices and also satisfy the Basel II use test requirements with regards to model outputs, i.e. model outputs are actively used in risk management and performance measurement. / Thesis (Ph.D. (Risk management))--North-West University, Potchefstroom Campus, 2012.

Basel II provides banks with four options that may be used to calculate regulatory capital for operational risk. Each of these options (except the most basic approach) requires an underlying risk measurement and management system, with increasing complexity and more refined capital calculations under the more advanced approaches. Approaches available are BIA, TSA, ASA and AMA. The most advanced and complex option under Basel II is the AMA. This approach allows a bank to calculate its regulatory and economic capital requirements (using internal models) based on internal risk variables and profiles, rather than exposure proxies like gross income. This is the only risk–sensitive approach allowed by and described in Basel II. Accompanying internal models, complex and sophisticated measurement instruments, risk management processes and frameworks, as well as a robust governance structure need to be implemented. This study focuses on the practical design and implementation of an AMA capital model. This includes a beginning–to–end solution for capital modelling and covers all elements of data analysis, capital calculation and capital allocation. The proposed capital model is completely risk–based, leading to risk–sensitive capital calculations and allocations for all business lines in a bank. The model was constructed to comply fully with all Basel II requirements and standards. The proposed model was subsequently applied to one South African bank’s operational risk data, i.e. risk scenario and internal loss data of the bank were used as inputs into the proposed capital model. Regulatory capital requirements were calculated for all business lines in the bank and for the bank as a whole on a group level. Total capital requirements were also allocated to all business lines in the bank. For regulatory capital purposes, this equated to the stand–alone capital requirement of each business line. Calculations excluded the modelling and incorporation of insurance, expected loss offsets and correlation. These capital mitigation techniques were, however, proposed as part of the comprehensive capital model. AMA based capital calculations for the bank’s business lines resulted in significant capital movements compared to TSA capital requirements for the same calculation periods. The retail banking business line was allocated less capital compared to corresponding TSA estimates. This is mainly attributable to lower levels of tail risk exposure given high income levels (which are the bases for TSA capital calculations). AMA–based capital for the investment banking business line was higher than corresponding TSA estimates, due to high levels of extreme risk exposure relative to income generated. Employing capital modelling results in operational risk management and performance measurement was discussed and proposals made. This included the use of capital requirements (modelling results) in day–to–day operational risk management and in strategic decision making processes and strategic risk management. Proposals were also made on how to use modelling results and capital allocations in performance measurement. It was proposed that operational risk capital costs should be included in risk–adjusted performance measures, which can in turn be linked to remuneration principles and processes. Ultimately this would incentivise sound operational risk management practices and also satisfy the Basel II use test requirements with regards to model outputs, i.e. model outputs are actively used in risk management and performance measurement. / Thesis (Ph.D. (Risk management))--North-West University, Potchefstroom Campus, 2012.

A aplicação de Redes Bayesianas como modelo causal em Risco Operacional e extremamente atrativa do ponto de vista do gerenciamento dos riscos e do calculo do capital regulatorio do primeiro pilar do Novo Acordo da Basileia. Com as Redes e possível obter uma estimativa do VAR operacional utilizando-se não somente os dados históricos de perdas, mas também variáveis explicativas e conhecimento especialista através da possibilidade de inclusão de informações subjetivas. / The application of Bayesian Networks as causal model in Operational Risk is very attractive from the point of view of risk management and the calculation of regulatory capital under the first pillar of the New Basel Accord. It is possible to obtain with the networks an estimate of operational VAR based not only on the historical loss data but also in explanatory variables and expert knowledge through the possibility of inclusion of subjective information.

A aplicação de Redes Bayesianas como modelo causal em Risco Operacional e extremamente atrativa do ponto de vista do gerenciamento dos riscos e do calculo do capital regulatorio do primeiro pilar do Novo Acordo da Basileia. Com as Redes e possível obter uma estimativa do VAR operacional utilizando-se não somente os dados históricos de perdas, mas também variáveis explicativas e conhecimento especialista através da possibilidade de inclusão de informações subjetivas. / The application of Bayesian Networks as causal model in Operational Risk is very attractive from the point of view of risk management and the calculation of regulatory capital under the first pillar of the New Basel Accord. It is possible to obtain with the networks an estimate of operational VAR based not only on the historical loss data but also in explanatory variables and expert knowledge through the possibility of inclusion of subjective information.

The debate on quantitative operational risk modeling has only started at the beginning of the last decade and the best-practices are still far from being established. Estimation of capital requirements for operational risk under Advanced Measurement Approaches of Basel II is critically dependent on the choice of risk measure, which quantifies the risk exposure based on the underlying simulated distribution of losses. Despite its well-known caveats Value-at-Risk remains a predominant risk measure used in the context of operational risk management. We describe several serious drawbacks of Value-at-Risk and explain why it can possibly lead to misleading conclusions. As a remedy we suggest the use of coherent risk measures - and namely the statistic known as Expected Shortfall - as a suitable alternative or complement for quantification of operational risk exposure. We demonstrate that application of Expected Shortfall in operational loss modeling is feasible and produces reasonable and consistent results. We also consider a variety of statistical techniques for modeling of underlying loss distribution and evaluate extreme value theory framework as the most suitable for this purpose. Using stress tests we further compare the robustness and consistency of selected models and their implied risk capital estimates...