Abstract:
Beginning with the fact that performant strategies of the financial institutions have programmes and management procedures for the banking risks, which have as main objective to minimize the probability of risk generation and the bank’s potential exposure, this paper wants to present the operational risk management and quantification methods. Also it presents the modality of minimum capital requirement for the operational risk. Therefore, the first part presents the conceptual approach of the operational risks through the point of view of the financial institutions exposed to this type of risk. The second part describes the management and evaluation methods for the operational risk. The final part of this article presents the approach assumed by a financial institution with a precise purpose: the quantification of the minimum capital requirements of the operational risk.

Abstract:
Many banks adopt the Loss Distribution Approach to quantify the operational risk capital charge under Basel II requirements. It is common practice to estimate the capital charge using the 0.999 quantile of the annual loss distribution, calculated using point estimators of the frequency and severity distribution parameters. The uncertainty of the parameter estimates is typically ignored. One of the unpleasant consequences for the banks accounting for parameter uncertainty is an increase in the capital requirement. This paper demonstrates how the parameter uncertainty can be taken into account using a Bayesian framework that also allows for incorporation of expert opinions and external data into the estimation procedure.

Abstract:
The largest US banks are required by regulatory mandate to estimate the operational risk capital they must hold using an Advanced Measurement Approach (AMA) as defined by the Basel II/III Accords. Most use the Loss Distribution Approach (LDA) which defines the aggregate loss distribution as the convolution of a frequency and a severity distribution representing the number and magnitude of losses, respectively. Estimated capital is a Value-at-Risk (99.9th percentile) estimate of this annual loss distribution. In practice, the severity distribution drives the capital estimate, which is essentially a very high quantile of the estimated severity distribution. Unfortunately, because the relevant severities are heavy-tailed AND the quantiles being estimated are so high, VaR always appears to be a convex function of the severity parameters, causing all widely-used estimators to generate biased capital estimates (apparently) due to Jensen's Inequality. The observed capital inflation is sometimes enormous, even at the unit-of-measure (UoM) level (even billions USD). Herein I present an estimator of capital that essentially eliminates this upward bias. The Reduced-bias Capital Estimator (RCE) is more consistent with the regulatory intent of the LDA framework than implementations that fail to mitigate this bias. RCE also notably increases the precision of the capital estimate and consistently increases its robustness to violations of the i.i.d. data presumption (which are endemic to operational risk loss event data). So with greater capital accuracy, precision, and robustness, RCE lowers capital requirements at both the UoM and enterprise levels, increases capital stability from quarter to quarter, ceteris paribus, and does both while more accurately and precisely reflecting regulatory intent. RCE is straightforward to implement using any major statistical software package.

Abstract:
The final version of the New Capital Accord, which includes operational risk, was released by the Basel Committee on Banking Supervision in June 2004. The article “Basel II approaches for the calculation of the regulatory capital for operational risk” is devoted to the issue of operational risk of credit financial institutions. The paper talks about methods of operational risk calculation, advantages and disadvantages of particular methods.

Abstract:
The management of operational risk in the banking industry has undergone significant changes over the last decade due to substantial changes in operational risk environment. Globalization, deregulation, the use of complex financial products and changes in information technology have resulted in exposure to new risks very different from market and credit risks. In response, Basel Committee for banking Supervision has developed a regulatory framework, referred to as Basel II, that introduced operational risk category and corresponding capital requirements. Over the past five years, major banks in most parts of the world have received accreditation under the Basel II Advanced Measurement Approach (AMA) by adopting the loss distribution approach (LDA) despite there being a number of unresolved methodological challenges in its implementation. Different approaches and methods are still under hot debate. In this paper, we review methods proposed in the literature for combining different data sources (internal data, external data and scenario analysis) which is one of the regulatory requirement for AMA.

Abstract:
The objective of this paper is to provide a global perspective of the operational risk from a banking societies’ viewpoint. We describe the main regulations and settlements in the field and examine the various approaches of the operational banking risk. The paper presents the need of banks to managing operational risk. We study comparatively for a banking society the capital charge for covering the operational risk under the basic indicator approach and under the standardized approach. We present a case study of implementing current capital requirements at the level of a Romanian banking society. From the theoretical approach and from the description of quantifying of operational banking risk, the results of this study insist on the importance of measuring of operational banking risk and identifies major issues that need to be considered to improve the managing operational banking risk.

Abstract:
We study capital requirements for bounded financial positions defined as the minimum amount of capital to invest in a chosen eligible asset targeting a pre-specified acceptability test. We allow for general acceptance sets and general eligible assets, including defaultable bonds. Since the payoff of these assets is not necessarily bounded away from zero the resulting risk measures cannot be transformed into cash-additive risk measures by a change of numeraire. However, extending the range of eligible assets is important because, as exemplified by the recent financial crisis, assuming the existence of default-free bonds may be unrealistic. We focus on finiteness and continuity properties of these general risk measures. As an application, we discuss capital requirements based on Value-at-Risk and Tail-Value-at-Risk acceptability, the two most important acceptability criteria in practice. Finally, we prove that there is no optimal choice of the eligible asset. Our results and our examples show that a theory of capital requirements allowing for general eligible assets is richer than the standard theory of cash-additive risk measures.

Abstract:
The research sought to determine the role of capital on commercial bank performance in Zimbabwe. Descriptive correlation method was used in this research and the population includes senior commercial bank performance. Twenty executives were selected from each of the chosen banks and interviewed on various issues pertaining to bank capitalization and performance. This was augmented by some regression analysis to determine the magnitude of effect of capital on performance of selected banks. The banks were grouped into strata which were classified as undercapitalized, fairly capitalized and well capitalized as determined by the country’s central bank’s minimum capital levels of US$12.5 million for commercial banks. Findings revealed that there is a significant and positive relationship between commercial bank capitalization and its performance. The findings of this research cannot be generalized to all financial intermediaries let alone all companies since it had narrowed down to commercial banks. The research managed to elaborate on the relationship between capital levels and bank performance as well as the importance of capital to other bank operations. Other factors affecting bank performances were only highlighted, thus other studies can carried out which looks at those factors in detail for example the impact of internal control systems on bank stability and performance as well as the role played by non-interest income to overall bank profitability.

Abstract:
We set the context for capital approximation within the framework of the Basel II / III regulatory capital accords. This is particularly topical as the Basel III accord is shortly due to take effect. In this regard, we provide a summary of the role of capital adequacy in the new accord, highlighting along the way the significant loss events that have been attributed to the Operational Risk class that was introduced in the Basel II and III accords. Then we provide a semi-tutorial discussion on the modelling aspects of capital estimation under a Loss Distributional Approach (LDA). Our emphasis is to focus on the important loss processes with regard to those that contribute most to capital, the so called high consequence, low frequency loss processes. This leads us to provide a tutorial overview of heavy tailed loss process modelling in OpRisk under Basel III, with discussion on the implications of such tail assumptions for the severity model in an LDA structure. This provides practitioners with a clear understanding of the features that they may wish to consider when developing OpRisk severity models in practice. From this discussion on heavy tailed severity models, we then develop an understanding of the impact such models have on the right tail asymptotics of the compound loss process and we provide detailed presentation of what are known as first and second order tail approximations for the resulting heavy tailed loss process. From this we develop a tutorial on three key families of risk measures and their equivalent second order asymptotic approximations: Value-at-Risk (Basel III industry standard); Expected Shortfall (ES) and the Spectral Risk Measure. These then form the capital approximations.

The recent mortgage crisis has resulted in several bank failures. Under the current Basel I capital framework, banks are not required to hold a sufficient amount of capital to support the risk associated with their mortgage activities. The new Basel II capital rules are intended to be more risk based and would require the right amount of capital buffer to support bank risk. However, Basel II models could become too complex and too costly to implement, often resulting in a trade-off between complexity and model accuracy. Since the Basel II rules are meant to be principal based (rather than prescriptive), banks have the flexibility to build risk models that best fit their unique structure. We find that the variation of the model, particularly how mortgage portfolios are segmented, could have a significant impact on the default and loss estimated. This paper finds that the calculated Basel II capital varies considerably across the default prediction model and segmentation schemes, thus providing banks with an incentive to choose an approach that results in the least required capital for them. We find that a more granular segmentation model produces smaller required capital, regardless of the economic environment. Our results suggest that banks may have incentives to build risk models that meet the Basel II requirement and still yield the least amount of required capital.