Furthermore, we test whether these distributional scaling relations can be described using simple location-shift or location-scale shift models. We extend past studies of losses on firm size by applying quantile regression techniques to obtain a more complete view of scaling relationships across the loss distribution. He is a trained lawyer and often lectures on the subject of operational risk management at the Frankfurt School of Finance and Goethe Business School.We investigate whether the size of operational risk losses can be correlated with geographical region and firm size.
Robert also helped implement the advanced measurement approach across the organisation, which was approved in 2007 by the BAFin, the German Banking supervisor, for regulatory capital purposes. Since 1999 he has helped develop the Bank’s operational risk governance and framework. He joined Deutsche Bank in 1998, initially in charge of the Global New Product Approval framework. In 2003, Robert was Operational Risk Officer for the Corporate Finance division. In 2006 he became Head of the Regional Operational Risk Management Team in London, in charge of establishing the day-to-day operational risk management across Deutsche Bank’s Corporate Investment Bank. At Deutsche Bank, Robert is currently in charge of managing the Bank’s overall oper - ational risk profile and also supervising the operational risk profiles of the business and infrastructure divisions.
Robert Huebner is Deutsche Bank’s Deputy Head of Operational Risk Management and a Board Member of Operational Risk eXchange (ORX), the operational loss-data consortium, for which he chairs the Quality Assurance Working Group. Clear governance, systematic data reconciliation and quality assurance are key elements to establishing the foundations for operational risk managers to earn their seat at the decision-making table in a modern financial organisation. At the same time, it drives the continuous improvement of the loss-data capturing process with regards to completeness and correctness. This requires sophisticated capital modelling, plenty of correlation and root-cause research, and dedicated development of predictive and sensitive key risk indicators.Using loss-data creates transparency and validates many pre-perceptions around operational risk. The operational risk inherent in investments (such as acquisitions or new products) can now be priced, as well as divestments (such as outsourcings or reduced controls), and can provide a business case of prevented operational risk losses to justify IT developments or increased resources for new controls. This is despite the fact that operational risk within credit and market risk contributed to some of the biggest losses the industry has experienced over recent years.Based on the loss-data gathered over the last 10–15 years, the operational risk discipline has matured sufficiently to inform strategic and investment decision-taking, in the same way as credit or market risk has done for many years. Operational risk remains rather low correlated with credit and market risk, and thus hardly increased during the financial crisis.
Continuous infrastructure improvements (such as via lessons learned, concentration analysis or Six-Sigma projects) and diligent preparation of changes (especially outsourcings and new products) are the instruments of choice to do so.The common causes of operational risks continue to be mishaps in the client interface or trader fraud. It can now be demonstrated that the operational risk in processing transactions is containable. Using operational risk loss-data is paying off.
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