Journal of Operational Risk
Editor-in-chief: Marcelo Cruz
Volume 1, Number 3 (Fall 2006)
Welcome to the third issue of The Journal of Operational Risk. Themain focus of the Journal is research on the risk measurement and management of operational risk and to promote greater understanding of this new and fast growing area in risk.
This Journal also aims at being an exclusive forum for discussions on this subject as, until now, there has been no dedicated forum for operational risk technical papers. Research in operational risk is a growth field in both the financial industry and academia. There are currently many lines of research, most of them trying to overcome the challenges presented by the new regulatory standards created by the Basel II Accord. However, currently there is not a single forum for the debate of these ideas. The Journal of Operational Risk fulfills this much-needed role.
The Journal of Operational Risk is the vehicle for communicating results in the modeling and management of operational risk. Examples of some areas of interest are: statistical/actuarial methods and estimation issues, causal models, scenario analysis based models, Bayesian methods, uses of external data within the framework, etc. We also encourage you to submit papers on new ideas and research on subjects such as corporate governance, business continuity plans, enterprise-wide risk, financial crime and the development of controls to avoid them, insurance, etc.
At this moment I would like to say that I am impressed by the increasing number of high-quality submissions that The Journal of Operational Risk has been receiving. We thank the authors for their trust in this young publication and appreciate all the encouraging messages sent by a number of colleagues in the industry and academia. This gives us comfort and the confidence that we are taking the right steps to build this new global industry forum for operational risk.
In this third issue, we present three research papers in the main section. Two of them tackle one of the most difficult issues faced by financial institutions aiming at the AMA (advanced measurement approach), which is the aggregation of the different types of data (internal loss, external loss and scenario analysis) into a single measure.
In the first paper, The structural modeling of operational risk via Bayesian inference, Shevchenko and Wüthrich argue that Bayesian inference is a statistical technique well suited for combining expert opinions and historical data. The authors summarize ways of estimating prior parameters subjectively from opinions and go from there to use this to mix scenario data with the internal losses, also showing some examples.
In the second paper, Bayesian inference, Monte Carlo sampling and operational risk, Peters and Sisson closely follow the lines of the previous paper, providing an overview of the Bayesian approach and then expand the analysis to general families of non-conjugate severity distributions with particular interest in the g-and-h distribution. In the last paper of this issue, Operational risk class homogeneity, Piacenza et al present a model for testing operational loss data homogeneity based on cluster analysis.
Operational Risk Forum
This section is intended to provide a less formal forum on findings and ideas about operational risk without the academic rigor demanded in the main section. The mission of the Forum is to promote active discussions of current issues in operational risk. Articles submitted to this section should preferably not exceed 8,000 words.
Contributions to the Forum can be articles that seek to explain difficult, unclear but otherwise known concepts and results. The articles that we would like to see in the Forum are designed to be tutorial and highly educational in nature. The main goal of the submitted articles is to bring a higher level of understanding to both industry and academia on issues and topics that might not normally be readily and easily accessible to either side.
In this issue, Allan Grody writes about the issues and costs of dealing with reference data. Also, in a second piece,Mohammad Fheili discusses the development of human resources key risk indicators that can be integrated into the operational risk measurement framework.
In this section we will be reviewing new books and publications in operational risk.
Papers in this issue
Developing human resources key risk indicators – Know Your Staff (KYS) practices
The structural modeling of operational risk via Bayesian inference: combining loss data with expert opinions
Operational risk class homogeneity
Solving the reference data problem in financial services – are we on the right path?
Bayesian inference, Monte Carlo sampling and operational risk