Journal of Operational Risk

Welcome to the third issue of Volume 10 of The Journal of Operational Risk. This is a special issue in which two of our four papers come from the CFS Conference on Operational Risk: Management and Measurement, which was held at the Goethe University in Frankfurt. This is the second year in which we have made up a special issue of the journal containing papers from this conference. The guest editors, Thomas Kaiser and MarkWahrenburg, provide an introduction to these two excellent papers later in the issue, but first we present two other top-quality papers.

In the issue's first paper, "Mitigating rogue trading behavior by means of appropriate, effective operational risk management", Sebastian Rick and Gerrit Jan van den Brink suggest specific metrics for tracking violation of a bank's policies and procedures by employees. The authors show that it is very difficult for financial institutions to uncover such violations in a timely manner using regular methods. In order to be able to proactively manage these risks they propose the application of modern basic criminological assumptions to analyze the multicausal cause-effect relationship in the underlying risk origination process. Their analysis is performed based on Schneider's model, which is used to describe criminal behavior. Based on the results of that analysis they introduce a specific conceptual risk indicator design in order to approximate the underlying risk exposure by means of a linear function. As we always suggest to our journal's authors, Rick and van den Brink provide examples of risk indicators, tracking their value through time.

In our second paper, "Truncated lognormals as a power-law mimic in operational risk", Roberto Torresetti and Claudio Nordio show that in some cases operational loss data exhibits power law behavior in parts of the tail distribution - in some cases with sharp deviations on the right-hand side of the tail. Taking into account such deviations when modeling operational risk leads to big differences in value-at-risk estimates. In order to handle these cases better, the authors make use of the power-law mimicry properties of truncated lognormal distributions and demonstrate that operational risk data fits well in these cases.

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Marcelo Cruz

Guest Editorial by Thomas Kaiser and Mark Wahrenburg

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