Journal of Operational Risk
ISSN:
1744-6740 (print)
1755-2710 (online)
Editor-in-chief: Marcelo Cruz
Volume 8, Number 4 (December 2013)
Editor's Letter
On March 22, 2013 a CFS Conference on Operational Risk (Management and Measurement) took place at the House of Finance at Goethe University in Frankfurt, Germany. It was organized jointly by the Center for Financial Studies (CFS) at Goethe University and the Fraunhofer Institute for Industrial Mathematics. Four authors in this issue have been selected based on the papers they submitted. They presented on current research on operational risk management and measurement. Unsurprisingly, the focus of the presentations was on the measurement part of the conference rather than on the management part. Additional articles were also received after a request for papers was issued by the CFS.
In his paper "How much should creditors worry about operational risk? The credit default swap spread reaction to operational risk events", Philip Sturm analyzes the CDS market's reaction to operational risk events in the banking industry, ie, he demonstrates the impact of operational risk on the default risk of banks.
In "A simple model for pseudo-nonstationarity in operational risk loss data due to interest rate dependency and reporting threshold", Gerrit Arlt, Frank Neumann and Udo Milkau demonstrate that some of the nonstationarity observed in operational risk loss data stems from changes in interest rates and from the impact of reporting thresholds.
Our third paper, "Closed-form approximations for operational value-at-risk" by Lorenzo Hernández, Jorge Tejero, Alberto Suárez and Santiago Carrillo-Menéndez, is about the application of computationally cheap approximation techniques derived from perturbation theory to the calculation of operational risk value-at-risk.
In "A Bayesian approach to extreme value estimation in operational risk modeling", Bakhodir Ergashev, Stefan Mittnik and Evan Sekeris propose a new approach for estimating operational risk models under the loss distribution approach. Their approach is based on the application of the Bayesian parameter estimation method to extreme value distribution.
The results of an empirical study on the effects of dependencies on aggregation of individual cell risks in the loss distribution approach to quantify operational risk are outlined in "Operational risk dependencies and the determination of risk capital" by Stefan Mittnik, Sandra Paterlini and Tina Yener. The conference's best paper award was granted to Sandra Paterlini for this paper.
Our fifth paper, "Modeling dependence of operational loss frequencies" by Eike Christian Brechmann, Claudia Czado and Sandra Paterlini, introduces a model that captures zero inflation and flexible dependencies.
The conference closed with an extensive panel discussion: "Potential Ways Forward". The key topic of discussion was how the discipline of operational risk is likely to develop in the coming years, and what contributions academics and practitioners could make to that development. While there was some controversy over the question of how much approaches to operational risk measurement will converge in the coming years, all panelists and conference participants shared the view that operational risk is going to become more important in the near future.
The CFS Conference on Operational Risk (Management and Measurement) is scheduled to be repeated biannually. The organizers (and guest editors of this issue of The Journal of Operational Risk) hope that the next conference sees broader coverage of all areas of operational risk management and measurement.
While the introduction of AMA 2.0 style models in some banks in Europe and elsewhere has demonstrated the need for further development of quantitative techniques for operational risk, both regulators and senior management must put more focus on use test issues these days. Enhancing the qualitative components of an operational risk framework is needed to go alongside refined governance. The various approaches that banks have come up with have been primarily based on in-house, hands-on methods that they have developed with little or no influence from academia. With the growing importance and increasing complexity of operational risk (taking into account such issues as risk culture, incentive and compensation systems, alignment of operational risk management with compliance, business continuity management, and similar areas), there is evidently a need for further research in this area
Thomas Kaiser
Goethe University and KPMG
Peter Ruckdeschel
Fraunhofer ITWM
Nataliya Horbenko
KPMG
Papers in this issue
A simple model for pseudo-nonstationarity in operational risk loss data due to interest rate dependency and reporting threshold
Modeling dependence of operational loss frequencies
A Bayesian approach to extreme value estimation in operational risk modeling
Closed-form approximations for operational value-at-risk
Operational risk dependencies and the determination of risk capital
How much should creditors worry about operational risk? The credit default swap spread reaction to operational risk events