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
Ten laws of operational risk
This paper sets out ten laws that govern the behavior of operational risk relating to the occurrence and detection/duration of events; the rapidity with which firms suffer losses; the lags in crystallization of losses; and internal and external drivers…
Quantification of regulatory capital for management of operational risk in banks: study from an emerging market economy
This paper studies the various methodologies used by an Indian bank in its operational risk management activities: these include loss database analysis, risk control self-assessment and key risk indicator (KRI) identification.
Evaluating cyclic risk propagation through an organization
Many large organizations have risk that propagates because of the dependencies between their various major organizational components. This paper addresses when cycles of dependencies exist in an organization or system of systems.
Does the source of information influence depositors’ withdrawal intentions during operational events?
The objective of this paper is to identify whether depositors’ intentions to withdraw funds during operational risk events differ based on the source of information.
Benchmarking operational risk stress testing models
This paper outlines several approaches to benchmarking operational loss projections under stressed scenarios using both accounting metrics and historical loss experience.
What is essential is invisible to the eye: prioritizing near misses to prevent future disasters
Near misses represent a primary information source to analyze the operational risk exposure of a company, since they can reveal gaps in the control environment. The model proposed in this paper aims at identifying the most dangerous events that could…
Strategic and technology risks: the case of Co-operative Bank
This paper studies the growth by acquisition strategy embarked upon by a mid-sized UK bank, the Co-operative Bank; this strategy was a disaster, leaving a heretofore successful bank in dire trouble and on the block for buyers at a substantial discount to…
An emergent taxonomy for operational risk: capturing the wisdom of crowds
In this paper, the author takes a data-driven approach and combines the individual active taxonomies of sixty large financial institutions (fifty-eight for construction and two for validation) to create a coherent new reference taxonomy: the ORX…
What do risk disclosures reveal about banking operational risk processes? Content analysis of banks’ risk disclosures in the Visegrad Four countries
Risk capital reserve and measurement precision in modeling heavy-tailed single operational losses
This paper provides a rationale for adopting quantitative buffer capital, designed to absorb variations due to measurement errors, especially those originating from the estimation risk.
Difference between the determinants of operational risk reporting in Islamic and conventional banks: evidence from Saudi Arabia
In this study, the author investigates the operational risk reporting practices of Islamic banking institutions (IBIs) and conventional banks (CBs) in Saudi Arabia. Moreover, the author explores the joint effect of banking characteristics, corporate…
Estimation of value-at-risk for conduct risk losses using pseudo-marginal Markov chain Monte Carlo
The authors propose a model for conduct risk losses, in which conduct risk losses are characterized by having a small number of extremely large losses (perhaps only one) with more numerous smaller losses.
Cyber risk management: an actuarial point of view
This paper points out the peculiarities of cyber insurance contracts compared with the classical nonlife insurance contracts from both the insurer’s and the insured’s perspectives. The main actuarial principles that are fundamental to any valuation in a…
Measuring expected shortfall under semi-parametric expected shortfall approaches: a case study of selected Southern European/Mediterranean countries
In this paper, the authors investigate the applicability of semi-parametric approaches for estimating expected shortfall.
The impact of enterprise risk management on the performance of companies in transition countries: Serbia case study
In this paper, seven hypotheses are defined, on the basis of which a theoretical model is developed to examine how different sources of enterprise risk affect the operational performance of Serbian companies and their risk of losing market position.
Applying existing scenario techniques to the quantification of emerging operational risks
This paper sets out techniques for: (a) identifying systematically emerging threats, their timescales, and interrelationships (eg, feedback loops and domino effects); (b) quantifying operational risks through structured scenario analysis processes that…
An investigation of cyber loss data and its links to operational risk
This paper investigates cyber loss data and focuses on quantifying the direct financial and compensatory losses emanating from cyber risks.
On the selection of loss severity distributions to model operational risk
This paper presents truncation probability estimates for loss severity data and a consistent quantile scoring function on annual loss data as useful severity distribution selection criteria that may stabilize regulatory capital.
The use of business intelligence and predictive analytics in detecting and managing occupational fraud in Nigerian banks
The goal of this paper is to illustrate how Nigerian banks, and indeed banks elsewhere, can develop solutions that incorporate both BI and predictive analytics techniques in detecting, predicting, preventing and managing occupational fraud.
Quantification of operational risk: statistical insights on coherent risk measures
In this paper, the authors review some of the existing methods used to quantify operational risks in the banking and insurance industries.