Journal of Operational Risk
ISSN:
1744-6740 (print)
1755-2710 (online)
Editor-in-chief: Marcelo Cruz
About this journal
Of the main areas of risk management, operational risk has the shortest history, with the industry beginning to give it serious consideration only 25 years ago. In that time, the industry has made great strides in both the definition and quantification of operational risk. The Journal of Operational Risk has been publishing papers at the forefront of this development since its inception.
On the quantification side, significant progress has been made, with major banks disclosing their operational risk exposures on a yearly basis. For many financial institutions their operational risk exposure is higher than that of market and credit risks. One large operational risk event can be lethal to a financial firm. Operational risk is thus a key concern for the industry as well as for the regulators that supervise financial institutions.
On the definition side, the industry has recently introduced the concept of “Non-Financial Risk” encompassing not just the early definition of operational risk but other risks like strategic, people, cyber, IT, etc. A broader view of operational risk would also consider Enterprise Risk Management, Cyber Risk Management, Information Technology Risks, Data Quality Risks amongst others. The introduction of new technologies like machine learning, artificial intelligence alongside new quantification ideas makes operational risk an intriguing risk domain with a green field for development and implementation of new ideas and theories.
With that in mind, The Journal of Operational Risk welcomes papers on non-financial risks as well as topics including, but not limited to, the following.
- The modeling and management of operational risk;
- Recent advances in techniques used to model operational risk, e.g., copulas, correlation, aggregate loss distributions, Bayesian methods and extreme value theory;
- The pricing and hedging of operational risk and/or any risk transfer techniques;
- Data modeling external loss data, business control factors and scenario analysis;
- Models used to aggregate different types of data;
- Causal models that link key risk indicators and macroeconomic factors to operational losses;
- Regulatory issues, such as Basel II or any other local regulatory issue;
- Enterprise risk management;
- Cyber risk management;
- IT risk management (how systems errors/fails impact an organization and change their risk profile);
- Big data applications to non-financial risk;
- Artificial intelligence and machine learning applications to risk management;
- Qualitative analysis of non-financial risks.
Journal Metrics:
Journal Impact Factor: 0.645
5-Year Impact Factor: 0.488
CiteScore: 0.8
Latest papers
A comparison of numerical approaches to determine the severity of losses
Modeling operational risk for good and bad bank loans
The major sources of operational risk and the potential benefits of its management
Fuzzy methods for variable selection in operational risk management
Modeling macroeconomic effects and expert judgments in operational risk: a Bayesian approach
Estimating operational risk capital: the challenges of truncation, the hazards of maximum likelihood estimation, and the promise of robust statistics
Asymptotics for operational risk quantified with a spectral risk measure
Systemic operational risk: smoke and mirrors
Reconstructing heavy-tailed distributions by splicing with maximum entropy in the mean
Capital assessment of operational risk for the solvency of health insurance companies
A combination model for operational risk estimation in a Chinese banking industry case
Legal risk and compliance for banks operating in a common law legal system
Systemic operational risk: the UK payment protection insurance scandal
A nonparametric approach to analyzing operational risk with an application to insurance fraud
Combining scenario analysis with loss data in operational risk quantification
Treatment of the data collection threshold in operational risk: a case study using the lognormal distribution
Uncertainty modeling framework in operational risk
Computing the value-at-risk of aggregate severities
An efficient threshold choice for the computation of operational risk capital
Leadership and high-reliability organizations: why banks fail