Journal of Operational Risk

Aims & Scope

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.

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Source: OpenAlex · Note: citations accumulate over time so older years appear higher

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