The Basel Committee's 2014 revision of its operational risk capital framework, along with the multi-billion-dollar settlements that financial institutions had to make with financial authorities, has made operational risk the key focus of risk management. The Journal of Operational Risk stimulates active discussions of practical approaches to quantify, model and manage this risk, also discussing current issues in the discipline, and is essential reading for keeping practitioners and academics informed of the latest research in operational risk theory and practice.
The Journal of Operational Risk considers submissions in the form of research papers and forum papers, on the following, but not limited to, topics:
- Modelling and management of operational risk
- Recent advances in techniques used to model operational risk, for example: copulas, correlation, aggregate loss distributions, Bayesian methods and extreme value theory
- Pricing and hedging of operational risk and/or any risk transfer techniques
- Data modelling external loss data, business control factors and scenario analysis
- Models used to aggregate the 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
This paper shows that it is an "inconvenient truth" that the largest losses by banks are not firm specific.
This paper focuses on the distribution of correlations among aggregate operational risk losses.
Application of the convolution operator for scenario integration with loss data in operational risk modeling
This paper addresses the uncertainty in scenario analysis and produces a combined loss distribution.
This paper studies alternative mixing models for external data for a particular risk class.
This paper identifies three steps in sourcing risk.
This paper makes use of the power-law mimicry properties of the truncated lognormal distribution and shows how they fit operational risk data considerably well.
A weighted likelihood estimator for operational risk data: improving the accuracy of capital estimates by robustifying maximum likelihood estimates
This paper proposes the use of a robust generalization of MLEs for the modeling of operational loss data.
This paper discusses the violation of applicable firm guidelines by individuals employed by a bank or financial institution and suggests specific metrics to identify and prevent such behaviour.
This paper suggests an approach for assessing IT risk through an incident-based method for monitoring operational IT risk across an extended enterprise based on the ISACA Risk IT framework.
This paper proposes a methodology to frame risk self-assessment data into suitable prior distributions that can produce posterior distributions from which accurate operational risk measures.
A simple, transparent and rational weighting approach to combining different operational risk data sources
The authors propose a generic weighting function based on a nonparametric approach that can be used to weight the different distributions.
Approximations of value-at-risk as an extreme quantile of a random sum of heavy-tailed random variables
The authors of this paper study the approximation of extreme quantiles of random sums of heavy-tailed random variables. More specifically, sub-exponential random variables.
An assessment of the efficiency of operational risk management in Taiwan’s banking industry: an application of the stochastic frontier approach
Combining scenario and historical data in the loss distribution approach: a new procedure that incorporates measures of agreement between scenarios and historical data
Journal of Operational Risk, 10(1); 45-76
A checklist-based weighted fuzzy severity approach for calculating operational risk exposure on foreign exchange trades under the Basel II regime
The mutual-information-based variance–covariance approach: an application to operational risk aggregation in Chinese banking
Volume 9, Issue 2 (2014)