Journal of Operational Risk
ISSN:
1744-6740 (print)
1755-2710 (online)
Editor-in-chief: Marcelo Cruz
About this journal
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
Abstracting and Indexing: Scopus; Web of Science - Social Science Index; EconLit; and Cabell’s Directory
Journal Metrics:
Impact Factor: 0.677
5-Year Impact Factor: 0.85
CiteScore: 0.53
Latest papers
A note on the standard measurement approach versus the loss distribution approach–advanced measurement approach: the dawning of a new regulation
This paper presents a nonexhaustive review of the literature on operational risk quantification under a combination of the loss distribution approach model – the most commonly used of the AMA models – and extreme value theory.
Fast, accurate and straightforward extreme quantiles of compound loss distributions
In this paper, the author presents an easy-to-implement, fast and accurate method for approximating extreme quantiles of compound loss distributions (frequency + severity), which are commonly used in insurance and operational risk capital models.
Management of behavioral risk in the first line of defence
This paper discusses key features of fighting behavioral risk in the business line of operations as the central hub for all transactions in a bank.
Behavioral risks at the systemic level
By comparing the Libor and FX benchmark manipulation scandals, this paper describes how misbehavior emerged independently in both of these markets and the conditions that permitted the misconduct to survive and thrive.
The issues with the standardized measurement approach and a potential future direction for operational risk capital modeling
This paper discusses the criticism and praise the SMA and AMA have received, respectively, in many recent articles.
An operational risk-based regime-switching model for stock prices
This paper proposes a new risk-based regime-switching model for stock prices to examine the impact of operational risk events on stock prices.
A note on the statistical robustness of risk measures
This paper focuses on the parametric estimators of risk measures and uses Hampel’s infinitesimal approach to derive the robustness properties.
On a family of weighted Cramér–von Mises goodness-of-fit tests in operational risk modeling
This paper applies classical theory to determine if limiting distributions exist for WCvM test statistics under a simple null hypothesis.
Various approximations of the total aggregate loss quantile function with application to operational risk
This paper investigates the mechanics of the empirical aggregate loss bootstrap distribution.
A structural model for estimating losses associated with the mis-selling of retail banking products
In this paper, a structural model is presented for estimating losses associated with the mis-selling of retail banking products. It is the first paper to consider factor-based modeling for this operational/conduct risk scenario.
Standardized measurement approach: is comparability attainable?
This paper considers the claim of improved comparability of SMA outcomes by considering the ability to compare “internal loss experience” between banks.
Operational risk and the three lines of defence in UK financial institutions: is three really the magic number?
This paper examines the three lines of defence in the context of ORM in UK financial institutions.
Hidden Markov regimes in operational loss data: application to the recent financial crisis
The authors propose a method to consider business cycles in the computation of capital for operational risk.
A nonlinear analysis of operational risk events in Australian banks
This paper proposes a methodology applied to complex systems to analyze operational risk events in Australian banks.
Operational risk models and asymptotic normality of maximum likelihood estimation
In this paper, the author studies how asymptotic normality does, or does not, hold for common severity distributions in operational risk models.
The death of one thousand flowers or the AMA reborn?
The author of this paper explores the reasons for the pending demise of the advanced measurement approach (AMA) to operational risk.
Optimal B-robust posterior distributions for operational risk
The aim of this paper is to integrate prior information into a robust parameter estimation via OBR-estimating functions.
The benefit of using random matrix theory to fit high-dimensional t-copulas
This paper uses simulation studies and an example of operational risk modeling to show the necessity and benefit of using RMT to fit high-dimensional t-copulas in risk modeling.
Operational risk and the Solvency II capital aggregation formula: implications of the hidden correlation assumptions
The authors of this paper analyze the Solvency II standard formula for capital risk aggregation in relation to the treatment of operational risk capital.
An assessment of operational loss data and its implications for risk capital modeling
The author of this paper assesses operational loss data and its implications for risk capital modeling.
