Journal of Operational Risk

As the end of the year approaches and the arrival of the holiday season is felt all over the world, risk managers in the United States are not able to fully enjoy the festivities as they need to work long hours to prepare the so-called Comprehensive Capital Analysis and Review (a process they have had to undertake for the last four years). Since 2009 large financial institutions have been required by the Federal Reserve to perform this comprehensive annual "what-if" stress test, in which the state of the economy is assessed according to the behavior of a few macroeconomic factors and financial indicators. Banks are required to take this information and analyze how another crisis would affect their earnings and capital ratios. While this is reasonably straightforward for market risk and credit risk, for operational risk it is not an easy job, particularly bearing in mind the poor quality of most loss databases. Given that many banks are trying to find correlations between these macroeconomic factors and operational risk, and also working on how to incorporate these findings into their modeling frameworks, there is significant interest in the subject across the industry. In this issue of the journal we have one paper that proposes a method for including macroeconomic factors in operational risk modeling, but we would like to see many more articles (technical or otherwise) discussing the subject.

I would like to ask potential authors to continue to submit to the journal on matters pertaining to the state of operational risk research. I would like to again emphasize that the journal is not solely for academic authors. Please note that we do publish papers that do not have a quantitative focus; indeed there is one example in this issue. We at The Journal of Operational Risk would be happy to see more submissions containing practical, current views on relevant matters as well as papers focusing on the technical aspects of operational risk.

In this issue we have three Research Papers as well as one paper in the Forum section.


The first research paper is "Modeling macroeconomic effects and expert judgments in operational risk: a Bayesian approach" by Holger Capa Santos, Marie Kratz and Franklin Mosquera Muñoz. This paper comes at a very timely moment, as banks in the United States are working on their Comprehensive Capital Analysis and Review stress tests and trying to figure out an elegant method for incorporating macroeconomic effects into their operational risk capital models. This subject is one of the hottest in the industry at the moment. Here, the authors consider a general Bayesian framework that not only adds information from operational losses, as traditional operational risk models do, but also incorporates market risk profiles and experts' opinions and takes into account the general macroeconomic environment. The aim of their model is to estimate a characteristic parameter of the operational risk severity distribution function using the listed sources of information. The paper follows a similar approach to an earlier paper in this journal by Lambrigger, Shevchenko and Wüthrich ("The quantification of operational risk using internal data, relevant external data and expert opinion", Volume 2(3) (Fall 2007), pp. 3-27), in which the authors analyze the influence of external information on operational risk models. Here, Capa Santos et al extend this analysis to macroeconomic effects. Interestingly enough, their theoretical model suggests that the severity of operational losses is more closely related to the macroeconomic environment than is usually assumed.

In the second technical paper, "Fuzzy methods for variable selection in operational risk management", Paola Cerchiello and Paolo Giudici give results from their research on the application of fuzzy logic techniques in operational risk, illustrating how fuzzy logic can be helpful in constructing event type variables in operational risk management. Although operational risk databases cannot be considered to be "native fuzzy", the authors show that modeling them according to fuzzy intervals can be advantageous as it allows us to incorporate more information into the model and also makes it easier to benchmark the performance of predictive models.

In our third research paper, "Modeling operational risk for good and bad bank loans", Dror Parnes provides an interesting view on a topic very closely related to the financial crisis: errors in the granting of loans. Parnes discusses the operational risks associated with type II errors in the typical lending decisions of banks. During these events, loan officers misidentify healthy borrowing firms that are not destined to default and wrongly reject their legitimate loan requests. These periodic mistakes presumably carry opportunity costs for the lending institutions in the form of a loss of profitable business. The author also explores the operational risks associated with the corresponding type I errors. During these events, loan officers fail to identify borrowing firms that will eventually go bankrupt and wrongly approve their illegitimate loan applications. These occasional miscalculations naturally transform into future financial losses to the lending institutions. The author illustrates several schemes for identifying these problems, analyzes their expected failure rates, compares their functionality, and proposes additional functionalities within these models for broad use by banks and other lending institutions.


There is only one paper in the forum section of this issue: "The major sources of operational risk and the potential benefits of its management" by Wael Hemrit and Mounira Ben Arab. In this paper the authors argue that the definition of operational risk given by Basel II is problematic when applied to institutions, since the risk only represents a potential loss. Staff and systems are considered to be the causes of losses, but this broad definition of risk does not take into account the fact that these entities are best placed to identify the sources of potential losses and to issue warnings in order to measure and manage this risk. This paper aims to identify the main sources of operational risk and to explain the potential benefits of managing such risk.

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