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: 0.4
5-Year Impact Factor: 0.6
CiteScore: 0.8
Latest papers
Unraveling Lebanon’s financial crisis: the path from promise to peril, delving into a risk strategist’s own experience
The author investigates the causes of Lebanon's financial crisis which began in 2019 and puts forward suggestions with which to restore trust and stability.
Cyber risk assessment model for information assets: a tailored approach for the financial and banking sector
The authors present a novel model risk assessment model designed specifically for cyber risks and information assets,
Artificial intelligence in crisis management: a bibliometric analysis
The authors carry out a bibliometric analysis of academic papers in the field of artificial intelligence applications in crisis management and propose potential new directions for researchers in this field.
A qualitative study of operational resilience in financial institutions
The authors analyze data from a qualitative survey of senior G-SIB employees to identify recommendations for organisations looking to improve their operational resilience.
How is risk culture conceptualized in organizations? The pan-industry risk culture (PIRC) model
This paper puts forward a pan-industry risk culture as a framework through which to proactively manage risk culture.
Natural language processing-based detection of systematic anomalies among the narratives of consumer complaints
The authors develop a means to detect nonmeritorious consumer complaints using natural language processing.
Do government audits raise the risk awareness of management? An investigation from the perspective of cost variability
The authors investigate the impact of government audits on state-owned enterprises, finding they increase cost variability in these enterprises.
Integrating internal and external loss data via an equivalence principle
The authors put forward a means address data scarcity in operational risk modelling by supplementing internal loss data with external loss data.
Composite Tukey-type distributions with application to operational risk management
This paper investigates composite Tukey-type distributions and puts forward a new composite model, the improved flexibility of which is demonstrated.
Semi-nonparametric estimation of operational risk capital with extreme loss events
The authors put forward a means to estimate value-at-risk capital during extreme loss events which combines SNP estimation with EVT-POT theory.
The important role of information technology and internal auditing in risk management: evidence from Greece
The authors investigate the value of using information technologies in internal audit, finding that its effective use can help mitigate risks in business operations.
Estimating the probability of insurance recovery in operational risk
The authors put forward a novel methodology for the estimation of probability of insurance recovery.
Credible value-at-risk
This paper proposes a means to determine whether a a calculated VaR is "too large" and give a definition of this term within the context.
How does fintech affect the revenue and risk of commercial banks? Evidence from China
The authors use data from Chinese commercial banks to investigate relationships between the development and adoption of fintech and the revenue and risk of commercial banks.
Estimating the correlation between operational risk loss categories over different time horizons
The authors propose and demonstrate the value of a model with which mathematical techniques can be applied to analytically calculate means, variances and covariances more accurately than Monte Carlo simulations.
Legal risk management in the Polish banking sector
We carry out a review of the management of legal risk in Polish banks and use empirical research to demonstrate how these risks are managed.
How to choose the dependence types in operational risk measurement? A method considering strength, sensitivity and simplicity
The authors put forward a method for banks to choose the most appropriate dependence type based on an empirical analysis of the Chinese Operational Loss Database.
Operational risk and regulatory capital: do public and private banks differ?
The authors investigate relationships between operational risk and regulatory capital in Indian public and private banks.
A text analysis of operational risk loss descriptions
The authors put forward a workflow for using text analysis to identify underlying risks in operational risk event descriptions.
Integrating text mining and analytic hierarchy process risk assessment with knowledge graphs for operational risk analysis
This paper proposes a new method, entitled the risk-based knowledge graph, which is designed to make analysis of safety records from an operational risk perspective easier and more efficient.
