Measuring Operational Risk in Financial Institutions: Contribution of Credit Risk Modeling (original) (raw)
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Measuring operational risk in financial institutions
Applied Financial Economics, 2012
The scarcity of internal loss databases tends to hinder the use of the advanced approaches for operational risk measurement (Advanced Measurement Approaches (AMA)) in financial institutions. As there is a greater variety in credit risk modelling, this article explores the applicability of a modified version of CreditRisk+ to operational loss data. Our adapted model, OpRisk+, works out very satisfying Values-at-Risk (VaR) at 95% level as compared with estimates drawn from sophisticated AMA models. OpRisk+ proves to be especially worthy in the case of small samples, where more complex methods cannot be applied. OpRisk+ could therefore be used to fit the body of the distribution of operational losses up to the 95%-percentile, while Extreme Value Theory (EVT), external databases or scenario analysis should be used beyond this quantile.
Using Loss Data to Quantify Operational Risk
SSRN Electronic Journal, 2003
Management and quantification of operational risk has been impeded by the lack of internal or external data on operational losses. We consider newly available data collected from public information sources, and show how such data can be used to quantify operational risk for large internationally active banks. We find that operational losses are an important source of risk for such banks, and that the capital charge for operational risk will often exceed the charge for market risk. Although operational risk capital will vary depending on the size and scope of a bank's activities, our results are consistent with the 2-7 billion dollars in capital some large internationally active banks are currently allocating for operational risk.
Implications of Alternative Operational Risk Modeling Techniques
2005
Quantification of operational risk has received increased attention with the inclusion of an explicit capital charge for operational risk under the new Basle proposal. The proposal provides significant flexibility for banks to use internal models to estimate their operational risk, and the associated capital needed for unexpected losses. Most banks have used variants of value at risk models that estimate frequency, severity, and loss distributions. This paper examines the empirical regularities in operational loss data. Using loss data from six large internationally active banking institutions, we find that loss data by event types are quite similar across institutions. Furthermore, our results are consistent with economic capital numbers disclosed by some large banks, and also with the results of studies modeling losses using publicly available "external" loss data.
Routledge eBooks, 2017
The rapid growth of the banking business requires banks to adapt quickly and to be supported by reliable risk management. In contrast to the market and credit risks, an operational risk is the first risk type known by the banks, but the least understood compared to market and credit risks. Basel II (International Committee for setting up bank risk management) defines an operational risk as the arising risk from the failure of internal processes, people, systems, or external events. Basel II also sets the standard and internal calculation modelling that must be applied by the banks. This research discusses the method for a bank to measure the operational risk capital cost accurately with the Advanced Measurement Approach (AMA), that requires historical data (Loss Event Database) regarding operational loss events. This advanced approach uses mathematics and probabilistic calculation, that highly likely provides an accurate result. This research found that the Loss Distribution Approach has high accuracy for calculating operational risk on every event of the eight bank business lines. It is known that the largest fraud is derived from internal bank operation.
2012
The Basel II accord requires banks to put aside a capital buffer against unexpected operational losses, resulting from inadequate or failed internal processes, people and systems or from external events. Under the sophisticated Advanced Measurement Approach banks are given the opportunity to develop their own model to estimate operational risk. This report focus on a loss distribution approach based on a set of real data. First a comprehensive data analysis was made which suggested that the observations belonged to a heavy tailed distribution. An evaluation of commonly used distributions was performed. The evaluation resulted in the choice of a compound Poisson distribution to model frequency and a piecewise defined distribution with an empirical body and a generalized Pareto tail to model severity. The frequency distribution and the severity distribution define the loss distribution from which Monte Carlo simulations were made in order to estimate the 99.9% quantile, also known as the the regulatory capital. Conclusions made on the journey were that including all operational risks in a model is hard, but possible, and that extreme observations have a huge impact on the outcome.
An Approach to Modelling Operational Risk in Banks
WORKING PAPER SERIES-HENLEY …, 1999
While much work has been done in recent years on developing models to measure Market and Credit risks in banks and securities firms, there have been fewer attempts to model other important risks, in particular Operational Risk. This may be because, although recognised as important, there is little agreement on what constitutes Operational Risk. There is some agreement, however, that Operational Risk arises from a "breakdown" in operational processes and that Internal Audit has a key role in identifying potential operational breakdowns. This paper draws on the well-established theories of Reliability developed in Operational Research to propose models for estimating the likelihood of failures/breakdowns occurring in operational processes and for estimating the losses that might result from such breakdowns. Using these models, the paper demonstrates how a Value at Risk may be computed for the set of processes that comprise the operations of a bank. The role of Internal Audit in calibrating and testing such models is also highlighted.
Journal of Governance and Regulation (print)
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 da...
Operational Risk Management and Implications for Bank’s Economic Capital – a Case Study
2008
In this paper we review the actual operational data of an anonymous Central European Bank, using two approaches described in the literature: the loss distribution approach and the extreme value theory (“EVT”). Within the EVT analysis, two estimation methods were applied; the standard maximum likelihood estimation method and the probability weighted method (“PWM”). Our results proved a heavy-tailed pattern of
A loss distribution for operational risk derived from pooled bank losses
The Basel II accord encourages banks to develop their own advanced measurement approaches (AMA). However, the paucity of loss data implies that an individual bank cannot obtain a probability distribution with any reliability. We propose a model, targeting the regulator initially, by obtaining a probability distribution for loss magnitude using pooled annual risk losses from the banks under the regulator's oversight. We start with summarized loss data from 63 European banks and adjust the probability distribution obtained for losses that go unreported by falling below the threshold level. Using our model, the regulator has a tool for understanding the extent of annual operational losses across all the banks under its supervision. The regulator can use the model on an ongoing basis to make comparisons in year-on-year changes to the operational risk profile of the regulated banking sector. The Basel II accord lays out three possibilities for calculating the minimum capital reserve required to cover operational risk losses: the basic approach, the standardized approach, and the advanced measurement approach (AMA). The latter is specific to an individual bank that uses its own approach to determine capital requirements for its different lines of business and for the bank as a whole. A typical AMA model uses a probability distribution for loss per incident of a certain category and another for the number of incidents in that category, although there are other modeling approaches as well. A problem with this approach then is the paucity of loss data available for any particular bank to obtain such distributions. We obtain a probability distribution for operational risk loss impact using summarized results of pooled operational risk losses from multiple banks. Doing so allows us to derive simple AMA models for the regulators using data from the banks they oversee. One possibility is that the regulator can obtain an estimate for the capital requirement for a 'typical' bank under its supervision. We use data from 63 banks that the distribution fits annual losses very well. Moreover, we adjust for the fact that the regulator sees only losses above a certain threshold, say €10,000.
Towards a framework for operational risk management in the banking sector
PHD Thesis, 2022
The objective of this study is three-pronged. One, it investigates the factors that influence capital adequacy as measured by the covariates (exposure, frequency and severity) used in banking operations that accompany firms data-log loss reports. Two, it assesses the differential impact of discretionary (by adding artificial data) and non-discretionary (using real world data) loss disclosure on firms’ value-at-risk. R software is used to determine the value-at-risk. GLM and GAMLSS techniques are employed and subsequent tests of significance derive aforementioned influential factors, accompanied by a data augmentation algorithm in Matlab software to determine the differential impact of artificial and real world operational loss disclosures on firms’ performance in relation to meeting capital requirements. Three, it challenges firms’ risk-neutral assumption inherent in operational risk practice, asserting that; in theory, banking operations are more risk averse. Rattle software is used in a k-means cluster analysis method to determine whether controls compensate for persistent losses due to the firms’ natural risk aversion. The research arrived at estimates on the number of losses and their sizes; whereby exposure positively influences the risk ceded by the bank having “learned” from possible variations in past data, therefore improving operational risk management frameworks by introducing ex ante forward-looking components, whereas the addition of artificial data points by data augmentation circumvents former dilemmas of large and rare events so one can do more “learning”, notwithstanding the nature of the data’s suspect quality as they are constructs not observations. Nevertheless, the artificial intelligent EBOR framework’s performance improves on (Hoohlo (2014)’s applied data scaling and parametization techniques arrived at a proxy of about ZAR3B) former techniques for capital adequacy calculation of OpRisk opening up exploration modeling beyond historical accounts of significance to incorporate forward-looking aspects. Furthermore, checks and balances set up based on operational negligence slow down operational risk losses over time thereby establishing the move of firm risk tolerance levels away from risk neutrality, suggesting that banks are more risk averse.